diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..0ca7537
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,8 @@
+* text=auto
+*.txt text
+*.py text
+*.ipynb text
+*.c text
+*.h text
+*.jpg binary
+*.png binary
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..f53176d
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,171 @@
+GNU GENERAL PUBLIC LICENSE
+Version 3, 29 June 2007
+
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+END OF TERMS AND CONDITIONS
diff --git a/README.md b/README.md
index d9d14ad..351e18e 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,116 @@
-# bloatectomy
-A python package for removing duplicate text in clinical notes
+# Bloatectomy
+Bloatectomy: a method for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents. Takes in a list of notes or a single file (.docx, .txt, .rtf, etc) or single string to be marked for duplicates. Marked output and tokens are output.
+
+# Requirements
+- Python>=3.7.x (in order for the regular expressions to work correctly)
+- re
+- sys
+- pandas (optional, only necessary if using MIMIC III data)
+- docx (optional, only necessary if input or output is a word/docx file)
+
+# Installation
+using anaconda or miniconda
+```
+conda install -c summerkrankin bloatectomy
+```
+
+using pip via PyPI  
+make sure to install it to python3 if your default is python2
+```
+python3 -m pip install bloatectomy
+```
+using pip via github
+```
+python3 -m pip install git+git://github.com/MIT-LCP/mimic-code TBA
+```
+manual install by cloning the repository
+```
+git clone git://github.com/MIT-LCP/mimic-code TBA
+cd bloatectomy
+python3 setup.py install
+```
+
+# Examples
+To run bloatectomy on a sample string with the following options:
+- highlighting duplicates
+- display raw results
+- output file as html
+- output file of numbered tokens:
+
+```
+from bloatectomy import bloatectomy
+
+text = '''Assessment and Plan
+61 yo male Hep C cirrhosis
+Abd pain:
+-other labs: PT / PTT / INR:16.6//    1.5, CK / CKMB /
+ICU Care
+-other labs: PT / PTT / INR:16.6//    1.5, CK / CKMB /
+Assessment and Plan
+'''
+
+bloatectomy(text, style='highlight', display=True, filename='sample_txt_highlight_output', output='html', output_numbered_tokens=True)
+```
+To use with example text or load ipynb examples, download the repository or just the bloatectomy_examples folder
+```
+cd bloatectomy_examples
+from bloatectomy import bloatectomy
+
+bloatectomy('./input/sample_text.txt',
+            style='highlight', display=False,
+            filename='./output/sample_txt_highlight_output',
+            output='html',
+            output_numbered_tokens=True,
+            output_original_tokens=True)
+```
+
+# Documentation
+The paper is located at TBA
+
+```
+class bloatectomy(input_text,
+                  path = '',
+                  filename='bloatectomized_file',
+                  display=False,
+                  style='highlight',
+                  output='html',
+                  output_numbered_tokens=False,
+                  output_original_tokens=False,
+                  regex1=r"(.+?\.[\s\n]+)",
+                  regex2=r"(?=\n\s*[A-Z1-9#-]+.*)",
+                  postgres_engine=None,
+                  postgres_table=None)
+```
+## Parameters  
+**input_text**: file, str, list  
+An input document (.txt, .rtf, .docx), a string of text, or list of hadm_ids for postgres mimiciii database or the raw text.
+
+**style**: str, optional, default=`highlight`  
+Method for denoting a duplicate. The following are allowed: `highlight`, `bold`, `remov`.
+
+**filename**: str, optional, default=`bloatectomized_file`
+A string to name output file of the bloat-ectomized document.
+
+**path**: str, optional, default=`' '`  
+The directory for output files.
+
+**output_numbered_tokens**: bool, optional, default=`False`  
+If set to `True`, a .txt file with each token enumerated and marked for duplication, is output as `[filename]_token_numbers.txt`. This is useful when diagnosing your own regular expression for tokenization or testing the `remov` option for **style**.
+
+**output_original_tokens**: bool, optional, default=`False`  
+If set to  `True`, a .txt file with each original (non-marked) token enumerated but not marked for duplication, is output as `[filename]_original_token_numbers.txt`.
+
+**display**: bool, optional, default=`False`  
+If set to `True`, the bloatectomized text will display in the console on completion.
+
+**regex1**: str, optional, default=`r"(.+?\.[\s\n]+)"`  
+The regular expression for the first tokenization. Split on a period (.) followed by one or more white space characters (space, tab, line breaks) or a line feed character (`\n`). This can be replaced with any valid regular expression to change the way tokens are created.
+
+**regex2**: str, optional, default=`r"(?=\n\s*[A-Z1-9#-]+.*)"`  
+The regular expression for the second tokenization. Split on any newline character (`\n`) followed by an uppercase letter, a number, or a dash. This can be replaced with any valid regular expression to change how sub-tokens are created.
+
+**postgres_engine**: str, optional
+The postgres connection. Only relevant for use with the MIMIC III dataset. When data is pulled from postgres the hadm_id of the file will be appended to the `filename` if set or the default `bloatectomized_file`. See the jupyter notebook [mimic_bloatectomy_example](./bloatectomy_examples/mimic_bloatectomy_example.ipynb) for the example code.
+
+**postgres_table**: str, optional
+The name of the postgres table containing the concatenated notes. Only relevant for use with the MIMIC III dataset. When data is pulled from postgres the hadm_id of the file will be appended to the `filename` if set or the default `bloatectomized_file`. See the jupyter notebook [mimic_bloatectomy_example](./bloatectomy_examples/mimic_bloatectomy_example.ipynb) for the example code.
diff --git a/bloatectomy/__init__.py b/bloatectomy/__init__.py
new file mode 100644
index 0000000..d8ffd44
--- /dev/null
+++ b/bloatectomy/__init__.py
@@ -0,0 +1,2 @@
+# -*- coding: utf-8 -*-
+from .bloatectomy import bloatectomy
diff --git a/bloatectomy/bloatectomy.py b/bloatectomy/bloatectomy.py
new file mode 100644
index 0000000..1063f77
--- /dev/null
+++ b/bloatectomy/bloatectomy.py
@@ -0,0 +1,249 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+
+#Bloatectomy: a method for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents.
+#Copyright (C) 2020  Summer K. Rankin, Roselie A. Bright, Katherine R. Dowdy
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+
+#    You should have received a copy of the GNU General Public License
+#    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+#Authors: Summer K. Rankin summerkrankin@gmail.com, Roselie A. Bright roselie.bright@fda.hhs.gov, Katherine R. Dowdy katerdowdy@gmail.com
+
+import re
+import sys
+
+class bloatectomy():
+    def __init__(self, input_text,  path = '', filename='bloatectomized_file',
+                 display=False, style='highlight', output='html', output_numbered_tokens=False, output_original_tokens=False,
+                 regex1=r"(.+?\.[\s\n]+)", regex2=r"(?=\n\s*[A-Z1-9#-]+.*)", postgres_engine=None, postgres_table=None):
+
+        self.path = path
+        self.filename = filename
+        self.display = display
+        self.style = style # =['highlight','bold','remove']
+        self.output_numbered_tokens  = output_numbered_tokens
+        self.output_original_numbered_tokens  = output_original_tokens
+        self.output = output
+        self.regex1 = regex1
+        self.regex2 = regex2
+        self.postgres_table = postgres_table
+        self.engine  = postgres_engine
+
+        assert float(sys.version[0:3]) >= 3.7, "Must use python 3.7.0 or higher for the regular expressions to work correctly."
+
+        try:
+            if  input_text.split('.')[1] == 'docx' or input_text.split('.')[1] == 'doc':
+                import docx
+                doc = docx.Document(input_text)
+                fullText = []
+                for para in doc.paragraphs:
+                    fullText.append(para.text)
+                self.input_text = '\n'.join(fullText)
+                print(style + "ing duplications in " + input_text + ". Output file = " + path + filename + '.' + output)
+                bloatectomy.main(self)
+
+            elif input_text.split('.')[1] == 'txt' or input_text.split('.')[1] == 'rtf':
+                with open(input_text) as file:
+                    self.input_text = file.read()
+                print(style + "ing duplications in " + input_text + ". Output file = " + path + filename + '.' + output)
+                bloatectomy.main(self)
+
+            else:
+                assert type(input_text) == str, "unsupported format"
+                self.input_text = input_text
+                print(style + "ing duplications. Output file = " + path + filename + '.' + output)
+                bloatectomy.main(self)
+
+        except IndexError:
+            assert type(input_text) == str, "unsupported format"
+            self.input_text = input_text
+            print(style + "ing duplications. Output file = " + path + filename + '.' + output)
+            bloatectomy.main(self)
+
+        except AttributeError:
+            import numpy
+            import pandas as pd
+            assert (type(input_text) == list or type(input_text) == numpy.ndarray), "unsupported format"
+            print('pulling notes from postgres database')
+            # select one of the hadm_id s in the list and get the concatenated notes for that hadm_id
+            for i in input_text:
+                print(style + "ing duplications in ID " + str(i))
+                # in this table, we have concatenated all notes from each hadm_id into a single document
+                query = """SELECT text FROM {0} WHERE hadm_id IN ({1})"""
+                query = query.format(self.postgres_table, i)
+                pt_text = pd.read_sql(query, self.engine)
+                self.input_text = ''
+                self.input_text = pt_text.text.to_list()[0]
+                self.filename = ''
+                self.filename = filename + '_' + str(i)
+                bloatectomy.main(self)
+                print("Output file = " + path + self.filename + '.' + output)
+
+    def main(self):
+        bloatectomy.tokenize_mark(self)
+        if self.output=='html':
+            bloatectomy.make_html(self)
+        else:
+            bloatectomy.make_docx(self)
+
+    def make_html(self):
+        """Takes the output of the just_replication_detection (list of strings) and returns an html file (in path + filename) for the admission with duplicates highlighted"""
+
+        file_name =  str(self.path) + str(self.filename) + '.html'
+        uniq = str("\n ".join(self.tokens))
+        # replace line feed characters with html linebreaks
+        uniq = uniq.replace("\n", "<br>")
+        # save bloatectomized file as an html
+        with open(file_name, "w") as file:
+            file.write(uniq)
+
+        if self.output_numbered_tokens == True:
+            with open(str(self.path) + str(self.filename) + '_token_numbers.txt',"w") as file:
+                for i in self.numbered_tokens:
+                 file.write(str("$ ".join(i) + '\n'))
+
+        if self.output_original_numbered_tokens == True:
+            with open(str(self.path) + str(self.filename) + '_original_token_numbers.txt',"w") as file:
+                for i in self.original_numbered_tokens:
+                 file.write(str("$ ".join(i) + '\n'))
+
+        if self.display==True:
+            print(uniq)
+
+    def make_docx(self):
+        """Takes the output of the just_replication_detection (list of strings) and returns a docx file (in path + filename) for the admission with duplicates highlighted"""
+        import docx
+        document = docx.Document()
+        p = document.add_paragraph()
+        run = p.add_run()
+        font = run.font
+        file_name =  str(self.path) + str(self.filename) + '.docx'
+        # adding the first entry, not duplicate by nature
+        p.add_run(self.tokens[0])
+        # iterating through all the tokens in text
+        for i in range(1, len(self.tokens)):
+            if bool(re.search('[<][m][a][r][k][>]', self.tokens[i])):
+                new_text = self.tokens[i].replace("<mark>", "").replace("</mark>", "")
+                run = p.add_run()
+                run.add_text(new_text)
+                run.font.highlight_color = docx.enum.text.WD_COLOR_INDEX.YELLOW
+                run.add_break(docx.enum.text.WD_BREAK.TEXT_WRAPPING)
+            elif bool(re.search('[<][b][>]', self.tokens[i])):
+                new_text = self.tokens[i].replace("<b>", "").replace("</b>", "")
+                run = p.add_run()
+                run.add_text(new_text)
+                run.font.bold = True
+                run.add_break(docx.enum.text.WD_BREAK.TEXT_WRAPPING)
+            else:
+                run = p.add_run()
+                run.add_text(self.tokens[i])
+                run.add_break(docx.enum.text.WD_BREAK.TEXT_WRAPPING)
+
+        document.save(file_name)
+        if self.display==True:
+            print(self.tokens)
+        if self.output_numbered_tokens == True:
+            with open(str(self.path) + str(self.filename) + '_token_numbers.txt',"w") as file:
+                for i in self.numbered_tokens:
+                    file.write(str("$ ".join(i) + '\n'))
+        if self.output_original_numbered_tokens == True:
+            with open(str(self.path) + str(self.filename) + '_original_token_numbers.txt',"w") as file:
+                for i in self.original_numbered_tokens:
+                 file.write(str("$ ".join(i) + '\n'))
+
+    def tokenize2(regex, token_in):
+        """
+        Tokenize (2nd time) on  a line feed character.
+        1. for each token, split if a line feed character is followed by
+        2. a capital letter, or a dash, or a number
+        """
+        tok_new = []
+        # find any \n followed by an uppercase letter, a number, or a dash
+        sent_token =re.split(regex, token_in)
+        # replace \n with a space with a space
+        sent_token = [re.sub(r"$\n+","",i) for i in sent_token] # remove from end
+        sent_token = [re.sub(r"^\n", "", i) for i in sent_token] #remove from front
+            # line feeds + whitespace or not
+        sent_token = [re.sub(r"\s+\n\s+", " ", i) for i in sent_token]
+        sent_token = [re.sub(r"\s+\n", " ", i) for i in sent_token]
+        sent_token = [re.sub(r"\n\s+", " ", i) for i in sent_token]
+        sent_token = [re.sub(r"\n", " ", i) for i in sent_token]
+        #remove front/end whitespace
+        sent_token = [i.strip(' ') for i in sent_token]
+        for i in sent_token:
+            if i != '':
+                tok_new.append(i)
+        return tok_new
+
+    def number_tokens(token):
+        """create a list of enumerated (numbered) tokens"""
+        for enum_num, enum_token in enumerate(token):
+            yield str(enum_num), enum_token
+
+    def tokenize_mark(self):
+        """
+        1. Take in raw text and do initial tokenization: on periods followed by one or more space, tab, or line feed character.
+        2. Secondary tokenization of each token on line feed character followed by a capital letter, or a number, or a dash.
+        3. Add tags to or remove duplicate tokens.
+        """
+        # tokenize 1
+        tok = re.split(self.regex1, self.input_text, flags=re.DOTALL)
+        # whitespace around tokens can cause a duplicate to be missed
+        tok = [i.strip(' ') for i in tok]
+        #tokenize 2
+        new_tok = []
+        for num, t in enumerate(tok):
+            n_tok = bloatectomy.tokenize2(self.regex2, t)
+            new_tok.extend(n_tok)
+        # save original data as numbered list
+        self.original_numbered_tokens = []
+        self.original_numbered_tokens = list(bloatectomy.number_tokens(new_tok))
+        # detect and mark/remove duplicates
+        self.tokens = []
+        self.tokens = list(bloatectomy.mark_duplicates(self, new_tok))
+        # save bloatectomized tokens as a numbered list
+        self.numbered_tokens = []
+        self.numbered_tokens = list(bloatectomy.number_tokens(self.tokens))
+
+    def mark_duplicates(self, input_tokens):
+        '''
+        Function uses a set() and list to generate each token with tags (of selected style) added to duplicate tokens.
+            INPUT: input_tokens = string of tokenized text (can be sentences, paragraphs, words etc)
+                   style = ['bold','higlight','remov'] what to do with duplicate text.
+            OUTPUT: yield a single token at a time (generator) until the end of the input_tokens.
+    '''
+        if self.style == 'bold':
+            tag = '<b>'
+            tag_end = '</b>'
+            remov = False
+        elif self.style == 'highlight':
+            tag = '<mark>'
+            tag_end = '</mark>'
+            remov = False
+        elif self.style == 'remov':
+            remov = True
+        else:
+            print("Select a style for duplicate text: 'bold' or 'highlight")
+        # create hash of tokens
+        tokens_set = set()
+        tokens_set_add = tokens_set.add
+        for token in input_tokens:
+            #skip any empty tokens
+            if token == '':
+                pass
+            elif token not in tokens_set:
+                tokens_set_add(token)
+                yield token
+            elif remov == False:
+                yield tag + token + tag_end
+            elif remov == True:
+                pass
diff --git a/bloatectomy_examples/bloatectomy_example.ipynb b/bloatectomy_examples/bloatectomy_example.ipynb
new file mode 100644
index 0000000..715240d
--- /dev/null
+++ b/bloatectomy_examples/bloatectomy_example.ipynb
@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bloatectomy import bloatectomy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Example using a string as input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = '''Assessment and Plan\n",
+    "61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and\n",
+    "renal failure of unclear duration.\n",
+    "Abd pain:\n",
+    "-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /\n",
+    "\n",
+    "Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,\n",
+    "ICU Care\n",
+    "-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /\n",
+    "   Communication:                                              Comments:\n",
+    "icu Care\n",
+    "Assessment and Plan \n",
+    "Chief Complaint:\n",
+    "61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and\n",
+    "renal failure of unclear duration.\n",
+    "Abd pain:\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "highlighting duplications. Output file = ./output/sample_string.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "bloatectomy(text, style='highlight', display=False, output='html', filename='./output/sample_string', output_numbered_tokens=True, output_original_tokens=True);\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "highlighting duplications. Output file = sample_string.html\n",
+      "Assessment and Plan<br> 61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.<br> Abd pain:<br> -other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /<br> Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,<br> ICU Care<br> <mark>-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /</mark><br> Communication:                                              Comments: icu Care<br> <mark>Assessment and Plan</mark><br> Chief Complaint:<br> <mark>61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.</mark><br> <mark>Abd pain:</mark>\n"
+     ]
+    }
+   ],
+   "source": [
+    "bloatectomy(text, style='highlight', display=True, output='html', filename='sample_string', output_numbered_tokens=True, output_original_tokens=True);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Example using a txt file as input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "highlighting duplications. Output file = ./output/sample_txt_highlight_output.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "bloatectomy('./input/sample_text.txt', style='highlight', display=False, filename='./output/sample_txt_highlight_output', output='html', output_numbered_tokens=True, output_original_tokens=True);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Example using an rtf file as input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "removing duplications. Output file = ./output/sample_rtf_remov_output.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "bloatectomy('./input/sample_text.rtf', style='remov', display=False, filename='./output/sample_rtf_remov_output', output='html', output_numbered_tokens=True, output_original_tokens=True);\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Example using a word document as input and output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bolding duplications. Output file = ./output/sample_docx_output.docx\n"
+     ]
+    }
+   ],
+   "source": [
+    "bloatectomy('./input/sample_text.docx', style='bold', display=False, filename='./output/sample_docx_output', output='docx');\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/bloatectomy_examples/bloatectomy_example.py b/bloatectomy_examples/bloatectomy_example.py
new file mode 100644
index 0000000..5f92421
--- /dev/null
+++ b/bloatectomy_examples/bloatectomy_example.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+
+
+text = '''Assessment and Plan
+61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and
+renal failure of unclear duration.
+Abd pain:
+-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+
+Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,
+ICU Care
+-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+   Communication:                                              Comments:
+icu Care
+Assessment and Plan
+Chief Complaint:
+61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and
+renal failure of unclear duration.
+Abd pain:
+'''
+
+bloatectomy(text, style='highlight', display=False, output='html', output_numbered_tokens=True, output_original_tokens=True)
+
+bloatectomy('sample_text.txt', style='highlight', display=False, filename='sample_txt_output', output='html', output_numbered_tokens=True, output_original_tokens=True)
+
+bloatectomy('sample_text.docx', style='bold', display=False, filename='sample_docx_output', output='docx')
diff --git a/bloatectomy_examples/input/sample_text.docx b/bloatectomy_examples/input/sample_text.docx
new file mode 100644
index 0000000..ba271f1
Binary files /dev/null and b/bloatectomy_examples/input/sample_text.docx differ
diff --git a/bloatectomy_examples/input/sample_text.rtf b/bloatectomy_examples/input/sample_text.rtf
new file mode 100644
index 0000000..2a5b7e8
--- /dev/null
+++ b/bloatectomy_examples/input/sample_text.rtf
@@ -0,0 +1,17 @@
+Assessment and Plan
+61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and
+renal failure of unclear duration.
+Abd pain:
+-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+
+Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,
+ICU Care
+-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+Communication:
+Comments:
+icu Care
+Assessment and Plan
+Chief Complaint:
+61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and
+renal failure of unclear duration.
+Abd pain:
diff --git a/bloatectomy_examples/input/sample_text.txt b/bloatectomy_examples/input/sample_text.txt
new file mode 100644
index 0000000..a79377c
--- /dev/null
+++ b/bloatectomy_examples/input/sample_text.txt
@@ -0,0 +1,17 @@
+Assessment and Plan
+61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and
+renal failure of unclear duration.
+Abd pain:
+-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+
+Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,
+ICU Care
+-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+Communication:
+Comments:
+icu Care
+Assessment and Plan 
+Chief Complaint:
+61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and
+renal failure of unclear duration.
+Abd pain:
\ No newline at end of file
diff --git a/bloatectomy_examples/mimic_bloatectomy_example.ipynb b/bloatectomy_examples/mimic_bloatectomy_example.ipynb
new file mode 100644
index 0000000..4d72157
--- /dev/null
+++ b/bloatectomy_examples/mimic_bloatectomy_example.ipynb
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bloatectomy on MIMIC III \n",
+    "+ Concatenating notes for each admission into one document\n",
+    "+ This concatenation step is necessary for running bloatectomy on the MIMICiii database. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os\n",
+    "import psycopg2\n",
+    "import numpy\n",
+    "from sqlalchemy import create_engine, update, event\n",
+    "from bloatectomy import bloatectomy "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# if a mistake is made the connection will need to be closed before running again. uncomment the lines below to reset\n",
+    "#conn.commit()\n",
+    "#cur.close()\n",
+    "#conn.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "POSTGRES_CONNECT = os.environ.get(\"POSTGRES_CONNECT\")\n",
+    "#or enter here in the format\n",
+    "# POSTGRES_CONNECT = psycopg2.connect(\"dbname=mimic user=postgres_username password=postgres_password options=--search_path=mimiciii\");\n",
+    "\n",
+    "POSTGRES_ENGINE = os.environ.get(\"POSTGRES_ENGINE\")\n",
+    "#or enter here in the format\n",
+    "# POSTGRES_ENGINE = create_engine('postgresql://postgres_username:postgres_password@localhost/mimic'\n",
+    "\n",
+    "#connect to posgres\n",
+    "conn = psycopg2.connect(POSTGRES_CONNECT)\n",
+    "engine = create_engine(POSTGRES_ENGINE)\n",
+    "cur = conn.cursor();\n",
+    "\n",
+    "#set search path for the mimic schema in postgres\n",
+    "cur.execute(\"\"\"SET search_path = mimiciii;\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  One Document per Admission\n",
+    "\n",
+    "For each admission, concatenate all the notes for that admission into one note (thus, each admission has one **document**). Create a table of these admission notes using the hospital admission id (hadm_id) as the identifier rather than the note id (row_id)\n",
+    "\n",
+    "### Notes by Admission `notes_concatenated` with or without metadata\n",
+    "+ group by admission ID\n",
+    "+ order by note date ('note_dt')\n",
+    "+ concatenate all notes for that admission ID into one string\n",
+    "+ metadata==True: concatenate all notes and other data (date(s), provider=cgid, note, type=category,description) for that admission ID into one string\n",
+    "+ save as notes_concatenated or notes_concatenated_metadata\n",
+    "\n",
+    "###  Create  new table for results `notes_concatenated` or `notes_concatenated_metadata`\n",
+    "+ hadm_id\n",
+    "+ text (concatenate notes and/or other data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set whether you want to include metadata at the top of each note (we don't use this for the NLP, but is' useful for the viewing by SMEs)\n",
+    "metadata = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if metadata==False:\n",
+    "    cur.execute(\"\"\"DROP TABLE IF EXISTS mimiciii.notes_concatenated;\n",
+    "    CREATE TABLE mimiciii.notes_concatenated\n",
+    "    (hadm_id int,\n",
+    "     text varchar);\"\"\") \n",
+    "else:\n",
+    "    cur.execute(\"\"\"DROP TABLE IF EXISTS mimiciii.notes_concatenated_metadata;\n",
+    "    CREATE TABLE mimiciii.notes_concatenated_metadata\n",
+    "    (hadm_id int,\n",
+    "     text varchar);\"\"\")\n",
+    "conn.commit();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# select the specific hadm_ids for this operation. Try with a few to make sure it's working\n",
+    "xf = pd.read_sql(\"\"\"\n",
+    "SELECT hadm_id\n",
+    "FROM mimiciii.noteevents LIMIT 10 \"\"\", engine)\n",
+    "\n",
+    "xf_ids = xf.hadm_id.unique()\n",
+    "#xf_ids"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### function that lets us make multiple  requests to the postgres using pandas read_sql"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@event.listens_for(engine, 'before_cursor_execute')\n",
+    "def receive_before_cursor_execute(conn, cursor, statement, params, context, executemany):\n",
+    "    if executemany:\n",
+    "        cursor.fast_executemany = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### function to pull notes, concatenate and save\n",
+    "\n",
+    "+ this will take a few hours to run for about half of the hadm_ids in the database\n",
+    "+ iterate through for each unique admission (hadm_id)\n",
+    "+ pull all notes for an admission\n",
+    "+ order notes by charttime , then storetime\n",
+    "+ concatenate\n",
+    "+ save as one big note to new table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for j in xf_ids:\n",
+    "    \n",
+    "    if metadata == False:\n",
+    "        table_name = 'notes_concatenated'\n",
+    "        sql = \"\"\"\n",
+    "        SELECT  hadm_id, chartdate, charttime, storetime, text\n",
+    "        FROM mimiciii.noteevents \n",
+    "            WHERE hadm_id in ({0})\n",
+    "        GROUP BY hadm_id, chartdate, charttime, storetime, text\n",
+    "        ORDER BY chartdate, charttime, storetime\"\"\"\n",
+    "\n",
+    "        # run sql query above to pull all notes for one admission (in order by date)\n",
+    "        sql = sql.format(j)\n",
+    "        xnotes=pd.read_sql(sql, engine)      \n",
+    "        xnotes = xnotes.loc[:,'text']\n",
+    "    \n",
+    "    else: \n",
+    "        table_name = 'notes_concatenated_metadata'\n",
+    "        sql = \"\"\"\n",
+    "        SELECT subject_id, hadm_id, chartdate, charttime, storetime, category, cgid, description, text\n",
+    "        FROM mimiciii.noteevents \n",
+    "            WHERE hadm_id in ({0})\n",
+    "        GROUP BY subject_id, hadm_id, chartdate, charttime, storetime, category, cgid, description, text\n",
+    "        ORDER BY chartdate, charttime, storetime\"\"\"\n",
+    "\n",
+    "        # run sql query above to pull all notes for one admission (in order by date)\n",
+    "        # concatenate notes and all other cols (metadata)\n",
+    "        # all the metadata gets put into one token for duplicate removal purposes\n",
+    "        sql = sql.format(j)\n",
+    "        xnotes=pd.read_sql(sql, engine)\n",
+    "        xnotes.loc[:,'text2'] = xnotes.loc[:,'text'] \n",
+    "        xnotes.iloc[:,-2] = '. '\n",
+    "        \n",
+    "    # put a a period + whitespace to designate the end start and end of a note     \n",
+    "    xnotes['separator'] = '. '\n",
+    "    xtext = xnotes.to_csv(None, header=False, index=False) \n",
+    "    # save as a new dataframe\n",
+    "    xtext2 = [(j, xtext)]\n",
+    "    xfulltext=pd.DataFrame(xtext2, columns=['hadm_id', 'text'])\n",
+    "    # append user and single note to the new table in database\n",
+    "    xfulltext.to_sql(table_name, con=engine, if_exists='append', chunksize=1, index=False, schema='mimiciii')\n",
+    "\n",
+    "conn.commit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the new table into pandas for inspection\n",
+    "notes_concat = pd.read_sql(\"\"\"\n",
+    "SELECT *\n",
+    "FROM mimiciii.notes_concatenated\"\"\", engine)\n",
+    "#notes_concat.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bloatectomy\n",
+    "Bloatectomize mimiciii data by passing a list of hadm_ids and the table name where the concatenated notes are located"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pulling notes from postgres database\n",
+      "highlighting duplications in ID 167853\n",
+      "Output file = ./output/mimic_167853.html\n",
+      "highlighting duplications in ID 107527\n",
+      "Output file = ./output/mimic_107527.html\n",
+      "highlighting duplications in ID 167118\n",
+      "Output file = ./output/mimic_167118.html\n",
+      "highlighting duplications in ID 196489\n",
+      "Output file = ./output/mimic_196489.html\n",
+      "highlighting duplications in ID 135453\n",
+      "Output file = ./output/mimic_135453.html\n",
+      "highlighting duplications in ID 170490\n",
+      "Output file = ./output/mimic_170490.html\n",
+      "highlighting duplications in ID 134727\n",
+      "Output file = ./output/mimic_134727.html\n",
+      "highlighting duplications in ID 114236\n",
+      "Output file = ./output/mimic_114236.html\n",
+      "highlighting duplications in ID 163469\n",
+      "Output file = ./output/mimic_163469.html\n",
+      "highlighting duplications in ID 189681\n",
+      "Output file = ./output/mimic_189681.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "bloatectomy(xf_ids, style='highlight', output='html', filename='./output/mimic', postgres_table='mimiciii.notes_concatenated', postgres_engine=engine);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pulling notes from postgres database\n",
+      "highlighting duplications in ID 167853\n",
+      "Output file = bloatectomized_file_167853.html\n",
+      "highlighting duplications in ID 167118\n",
+      "Output file = bloatectomized_file_167118.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "list_example = [167853,167118]\n",
+    "bloatectomy(list_example, style='highlight', output='html',  postgres_table='mimiciii.notes_concatenated', postgres_engine=engine);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/bloatectomy_examples/output/sample_docx_output.docx b/bloatectomy_examples/output/sample_docx_output.docx
new file mode 100644
index 0000000..7d0e359
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diff --git a/bloatectomy_examples/output/sample_rtf_remov_output.html b/bloatectomy_examples/output/sample_rtf_remov_output.html
new file mode 100644
index 0000000..3679f51
--- /dev/null
+++ b/bloatectomy_examples/output/sample_rtf_remov_output.html
@@ -0,0 +1 @@
+./input/sample_text.rtf
\ No newline at end of file
diff --git a/bloatectomy_examples/output/sample_rtf_remov_output_original_token_numbers.txt b/bloatectomy_examples/output/sample_rtf_remov_output_original_token_numbers.txt
new file mode 100644
index 0000000..926c7b4
--- /dev/null
+++ b/bloatectomy_examples/output/sample_rtf_remov_output_original_token_numbers.txt
@@ -0,0 +1 @@
+0$ ./input/sample_text.rtf
diff --git a/bloatectomy_examples/output/sample_rtf_remov_output_token_numbers.txt b/bloatectomy_examples/output/sample_rtf_remov_output_token_numbers.txt
new file mode 100644
index 0000000..926c7b4
--- /dev/null
+++ b/bloatectomy_examples/output/sample_rtf_remov_output_token_numbers.txt
@@ -0,0 +1 @@
+0$ ./input/sample_text.rtf
diff --git a/bloatectomy_examples/output/sample_string.html b/bloatectomy_examples/output/sample_string.html
new file mode 100644
index 0000000..545b38c
--- /dev/null
+++ b/bloatectomy_examples/output/sample_string.html
@@ -0,0 +1 @@
+Assessment and Plan<br> 61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.<br> Abd pain:<br> -other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /<br> Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,<br> ICU Care<br> <mark>-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /</mark><br> Communication:                                              Comments: icu Care<br> <mark>Assessment and Plan</mark><br> Chief Complaint:<br> <mark>61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.</mark><br> <mark>Abd pain:</mark>
\ No newline at end of file
diff --git a/bloatectomy_examples/output/sample_string_original_token_numbers.txt b/bloatectomy_examples/output/sample_string_original_token_numbers.txt
new file mode 100644
index 0000000..ca1999e
--- /dev/null
+++ b/bloatectomy_examples/output/sample_string_original_token_numbers.txt
@@ -0,0 +1,12 @@
+0$ Assessment and Plan
+1$ 61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.
+2$ Abd pain:
+3$ -other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+4$ Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,
+5$ ICU Care
+6$ -other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+7$ Communication:                                              Comments: icu Care
+8$ Assessment and Plan
+9$ Chief Complaint:
+10$ 61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.
+11$ Abd pain:
diff --git a/bloatectomy_examples/output/sample_string_token_numbers.txt b/bloatectomy_examples/output/sample_string_token_numbers.txt
new file mode 100644
index 0000000..4dbe7f9
--- /dev/null
+++ b/bloatectomy_examples/output/sample_string_token_numbers.txt
@@ -0,0 +1,12 @@
+0$ Assessment and Plan
+1$ 61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.
+2$ Abd pain:
+3$ -other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /
+4$ Troponin-T:4390/40/0.21, ALT / AST:258/575, Alk Phos / T Bili:232/5.9,
+5$ ICU Care
+6$ <mark>-other labs: PT / PTT / INR:16.6//                         1.5, CK / CKMB /</mark>
+7$ Communication:                                              Comments: icu Care
+8$ <mark>Assessment and Plan</mark>
+9$ Chief Complaint:
+10$ <mark>61 yo male Hep C cirrhosis and HCC presents with probable lower GIB and renal failure of unclear duration.</mark>
+11$ <mark>Abd pain:</mark>
diff --git a/bloatectomy_examples/output/sample_txt_highlight_output.html b/bloatectomy_examples/output/sample_txt_highlight_output.html
new file mode 100644
index 0000000..d9e49c6
--- /dev/null
+++ b/bloatectomy_examples/output/sample_txt_highlight_output.html
@@ -0,0 +1 @@
+./input/sample_text.txt
\ No newline at end of file
diff --git a/bloatectomy_examples/output/sample_txt_highlight_output_original_token_numbers.txt b/bloatectomy_examples/output/sample_txt_highlight_output_original_token_numbers.txt
new file mode 100644
index 0000000..29b5e69
--- /dev/null
+++ b/bloatectomy_examples/output/sample_txt_highlight_output_original_token_numbers.txt
@@ -0,0 +1 @@
+0$ ./input/sample_text.txt
diff --git a/bloatectomy_examples/output/sample_txt_highlight_output_token_numbers.txt b/bloatectomy_examples/output/sample_txt_highlight_output_token_numbers.txt
new file mode 100644
index 0000000..29b5e69
--- /dev/null
+++ b/bloatectomy_examples/output/sample_txt_highlight_output_token_numbers.txt
@@ -0,0 +1 @@
+0$ ./input/sample_text.txt
diff --git a/meta.yaml b/meta.yaml
new file mode 100644
index 0000000..7a970de
--- /dev/null
+++ b/meta.yaml
@@ -0,0 +1,44 @@
+{% set name = "bloatectomy" %}
+{% set version = "0.0.11" %}
+
+package:
+  name: "{{ name|lower }}"
+  version: "{{ version }}"
+
+source:
+  url: "https://pypi.io/packages/source/{{ name[0] }}/{{ name }}/{{ name }}-{{ version }}.tar.gz"
+  sha256: d9df69119581a5266058b9270d8851350c394cc28248f3eb9e3347445d68abdb
+  MD5: 65d618851c2584be59847a67dd468dbb
+  BLAKE2-256: 1309f91e59e3f5e7e800f5c204fe1c9442472ba1d667e527b859d6e46c631d4f
+
+build:
+  script: "{{ PYTHON }} -m pip install . -vv"
+  skip: true  # [py<37]
+
+requirements:
+  host:
+    - pip
+    - setuptools
+    - python
+  run:
+    - python
+
+test:
+  imports:
+    - bloatectomy
+  source_files:
+    - tests
+
+about:
+  home: "https://github.com/MIT-LCP/mimic-code"
+  license: GPL-3.0-or-later
+  license_family: GPL3
+  license_file: LICENSE.txt
+  summary: "Bloatectomy: a method for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents."
+  doc_url:
+  dev_url:
+
+extra:
+  recipe-maintainers:
+    - 1fmusic
+    - katerdowdy
diff --git a/paper/example_output.png b/paper/example_output.png
new file mode 100644
index 0000000..50d97b0
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diff --git a/paper/flowchart.png b/paper/flowchart.png
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index 0000000..97d9ff6
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diff --git a/paper/graph_abstract.png b/paper/graph_abstract.png
new file mode 100644
index 0000000..1164684
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diff --git a/paper/notes1.png b/paper/notes1.png
new file mode 100644
index 0000000..c4a1640
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diff --git a/paper/notes2.png b/paper/notes2.png
new file mode 100644
index 0000000..cd0ead1
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diff --git a/paper/output_notes1.png b/paper/output_notes1.png
new file mode 100644
index 0000000..d280728
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diff --git a/paper/output_notes2.png b/paper/output_notes2.png
new file mode 100644
index 0000000..dd43b48
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diff --git a/paper/paper.bib b/paper/paper.bib
new file mode 100644
index 0000000..da8da0f
--- /dev/null
+++ b/paper/paper.bib
@@ -0,0 +1,272 @@
+@electronic{Dean:2018,
+	Author = {Dean, Shannon M},
+	Title = {AHRQ Patient Safety Network: EHR Copy and Paste and Patient Safety},
+	Urldate = {Retrieved from https://psnet.ahrq.gov/perspectives/perspective/241}}
+
+@article{March:2016,
+  Author = {March, Christopher A and Scholl, Gretchen and Dversdal, Renee K and Richards, Matthew and Wilson, Leah M and Mohan, Vishnu and Gold, Jeffrey A},
+	Doi = {10.4300/JGME-D-15-00201.1},
+	Journal = {J Grad Med Educ},
+	Journal-Full = {Journal of graduate medical education},
+	Mesh = {Electronic Health Records; Humans; Internal Medicine; Internship and Residency; Simulation Training},
+	Month = {May},
+	Number = {2},
+	Pages = {237-40},
+	Pmc = {PMC4857515},
+	Pmid = {27168894},
+	Pst = {ppublish},
+	Title = {Use of Electronic Health Record Simulation to Understand the Accuracy of Intern Progress Notes},
+	Volume = {8},
+	Year = {2016},
+	Url = {https://doi.org/10.4300/JGME-D-15-00201.1}}
+
+@article{Tsou:2017,
+	Author = {Tsou, Amy Y and Lehmann, Christoph U and Michel, Jeremy and Solomon, Ronni and Possanza, Lorraine and Gandhi, Tejal},
+	Doi = {10.4338/ACI-2016-09-R-0150},
+	Journal = {Appl Clin Inform},
+	Journal-Full = {Applied clinical informatics},
+	Keywords = {Copy and paste; clinical documentation; electronic health records; health policy; information technology; patient safety},
+	Mesh = {Cooperative Behavior; Documentation; Electronic Health Records; Humans; Medical Informatics; Patient Safety; Stakeholder Participation},
+	Month = {01},
+	Number = {1},
+	Pages = {12-34},
+	Pmc = {PMC5373750},
+	Pmid = {28074211},
+	Pst = {epublish},
+	Title = {Safe Practices for Copy and Paste in the EHR. Systematic Review, Recommendations, and Novel Model for Health IT Collaboration},
+	Volume = {8},
+	Year = {2017},
+	Url = {https://doi.org/10.4338/ACI-2016-09-R-0150}}
+
+@article{Corwin:2004,
+	Author = {Corwin, Howard L and Gettinger, Andrew and Pearl, Ronald G and Fink, Mitchell P and Levy, Mitchell M and Abraham, Edward and MacIntyre, Neil R and Shabot, M Michael and Duh, Mei-Sheng and Shapiro, Marc J},
+	Doi = {10.1097/01.CCM.0000104112.34142.79},
+	Journal = {Crit Care Med},
+	Journal-Full = {Critical care medicine},
+	Mesh = {Adult; Aged; Anemia; Blood Chemical Analysis; Blood Transfusion; Cohort Studies; Confidence Intervals; Critical Care; Critical Illness; Erythrocyte Transfusion; Female; Follow-Up Studies; Humans; Intensive Care Units; Male; Middle Aged; Multivariate Analysis; Practice Patterns, Physicians'; Probability; Prospective Studies; Risk Assessment; Severity of Illness Index; Survival Analysis; Treatment Outcome; United States},
+	Month = {Jan},
+	Number = {1},
+	Pages = {39-52},
+	Pmid = {14707558},
+	Pst = {ppublish},
+	Title = {The CRIT Study: Anemia and blood transfusion in the critically ill--current clinical practice in the United States},
+	Volume = {32},
+	Year = {2004},
+	Url = {https://doi.org/10.1097/01.CCM.0000104112.34142.79}}
+
+@article{Cohen:2013,
+	Author = {Cohen, Raphael and Elhadad, Michael and Elhadad, No{\'e}mie},
+	Doi = {10.1186/1471-2105-14-10},
+	Journal = {BMC Bioinformatics},
+	Journal-Full = {BMC bioinformatics},
+	Mesh = {Algorithms; Data Mining; Electronic Health Records; Health Records, Personal; Humans; Semantics},
+	Month = {Jan},
+	Pages = {10},
+	Pmc = {PMC3599108},
+	Pmid = {23323800},
+	Pst = {epublish},
+	Title = {Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies},
+	Volume = {14},
+	Year = {2013},
+	Url = {https://doi.org/10.1186/1471-2105-14-10}}
+
+@article{Cohen:2014,
+	Author = {Cohen, Raphael and Aviram, Iddo and Elhadad, Michael and Elhadad, No{\'e}mie},
+	Doi = {10.1371/journal.pone.0087555},
+	Journal = {PLoS One},
+	Journal-Full = {PloS one},
+	Mesh = {Algorithms; Data Mining; Documentation; Electronic Health Records; Health Records, Personal; Humans; Models, Statistical},
+	Number = {2},
+	Pages = {e87555},
+	Pmc = {PMC3923754},
+	Pmid = {24551060},
+	Pst = {epublish},
+	Title = {Redundancy-aware topic modeling for patient record notes},
+	Volume = {9},
+	Year = {2014},
+	Url = {https://doi.org/10.1371/journal.pone.0087555}}
+
+@inproceedings{Ceglarek:2013,
+	Url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.677.2660&rep=rep1&type=pdf},
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+	Booktitle = {COGNITIVE 2013: The Fifth International Conference on Advanced Cognitive Technologies and Applications},
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+	Year = {2013}}
+
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+	Doi = {10.7326/0003-4819-157-1-201206190-00429},
+	Journal = {Ann Intern Med},
+	Journal-Full = {Annals of internal medicine},
+	Mesh = {Acute Coronary Syndrome; Adult; Blood Banks; Child; Decision Support Techniques; Erythrocyte Transfusion; Guideline Adherence; Hemoglobin A; Hospitalization; Humans; Randomized Controlled Trials as Topic; United States},
+	Month = {Jul},
+	Number = {1},
+	Pages = {49-58},
+	Pmid = {22751760},
+	Pst = {ppublish},
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+	Volume = {157},
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+	Url = {https://doi.org/10.7326/0003-4819-157-1-201206190-00429}}
+
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+	Author = {Bloomfield, LA},
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+	Year = {2011}
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+
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+
+@article{Altschul:1990,
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+	Doi = {10.1016/S0022-2836(05)80360-2},
+	Journal = {J Mol Biol},
+	Journal-Full = {Journal of molecular biology},
+	Mesh = {Algorithms; Amino Acid Sequence; Base Sequence; Databases, Factual; Mutation; Sensitivity and Specificity; Sequence Homology, Nucleic Acid; Software},
+	Month = {Oct},
+	Number = {3},
+	Pages = {403-10},
+	Pmid = {2231712},
+	Pst = {ppublish},
+	Title = {Basic local alignment search tool},
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+
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+  journal={Scientific data},
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+  pages={160035},
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+	}
+
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+	}
+
+@article{physionet,
+  title={PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals},
+  author={Goldberger, Ary L and Amaral, Luis AN and Glass, Leon and Hausdorff, Jeffrey M and Ivanov, Plamen Ch and Mark, Roger G and Mietus, Joseph E and Moody, George B and Peng, Chung-Kang and Stanley, H Eugene},
+  journal={Circulation},
+  volume={101},
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+  pages={e215--e220},
+  year={2000},
+  publisher={Am Heart Assoc}
+	}
+
+@book{python3,
+	 Doi = {10.5555/1593511},
+	 Author = {Van Rossum, Guido and Drake, Fred L},
+	 title = {Python 3 Reference Manual},
+	 year = {2009},
+	 isbn = {1441412697},
+	 publisher = {CreateSpace},
+	 address = {Scotts Valley, CA}
+	}
+
+@article{Lancichinetti:2015,
+	Author = {Lancichinetti, A., Sirer, M. I., Wang, J. X., Acuna, D., Koerding, K., & Amaral, L. A.},
+	Year = {2015},
+	Doi = {10.1103/PhysRevX.5.011007},
+  Title = {High-reproducibility and high-accuracy method for automated topic classification},
+	Journal = {Physical Review X},
+	Volume = {5},
+	Pages =  {011007-1--011007-11}
+	}
+@inproceedings{Mckinney:2010,
+	Author = {McKinney, W},
+	Year = {2010},
+	Title = {Data structures for statistical computing in python},
+	Booktitle = {Proceedings of the 9th Python in Science Conference},
+	Pages = {51--56}
+}
+@article{Meystre:2008,
+	Author = {Meystre, SM, and Savova, GK and Kipper-Schuler, KC and Hurdle, JF},
+	Year = {2008},
+	Title = {Extracting information from textual documents in the electronic health record: a review of recent research},
+	Journal = {Yearbook of medical informatics},
+	Volume = {17},
+	Number = {1},
+	Pages = {128--144}
+	}
+@misc{Mikolov:2013,
+	    title={Distributed Representations of Words and Phrases and their Compositionality},
+	    author={Tomas Mikolov and Ilya Sutskever and Kai Chen and Greg Corrado and Jeffrey Dean},
+	    year={2013},
+	    eprint={1310.4546},
+	    archivePrefix={arXiv},
+	    primaryClass={cs.CL}
+	}
+	@INPROCEEDINGS{Su:2008,
+	  author={Z. {Su} and B. {Ahn} and K. {Eom} and M. {Kang} and J. {Kim} and M. {Kim}},
+	  booktitle={2008 3rd International Conference on Innovative Computing Information and Control},
+	  title={Plagiarism Detection Using the Levenshtein Distance and Smith-Waterman Algorithm},
+	  year={2008},
+	  volume={},
+	  number={},
+	  pages={569-569}
+		}
+@article{Thielke:2007,
+		title = {Copying and pasting of examinations within the electronic medical record},
+		journal = {International Journal of Medical Informatics},
+		volume = {76},
+		pages = {S122--S128},
+		year = {2007},
+		note = {Information Technology in Health Care: Sociotechnical Approaches},
+		issn = {1386-5056},
+		doi = {10.1016/j.ijmedinf.2006.06.004},
+		url = {http://www.sciencedirect.com/science/article/pii/S1386505606001663},
+		author = {Thielke, S and Hammond, K and Helbig, S}
+	}
+@Article{Welch:1984,
+		  author={ {Welch}},
+		  journal={Computer},
+		  title={A Technique for High-Performance Data Compression},
+		  year={1984},
+		  volume={17},
+		  number={6},
+		  pages={8-19}
+			}
+@article{Wrenn:2010,
+			    author = {Wrenn, Jesse O and Stein, Daniel M and Bakken, Suzanne and Stetson, Peter D},
+			    title = "{Quantifying clinical narrative redundancy in an electronic health record}",
+			    journal = {Journal of the American Medical Informatics Association},
+			    volume = {17},
+			    number = {1},
+			    pages = {49-53},
+			    year = {2010},
+			    month = {01},
+			    issn = {1067-5027},
+			    doi = {10.1197/jamia.M3390},
+			    url = {https://doi.org/10.1197/jamia.M3390},
+			    eprint = {https://academic.oup.com/jamia/article-pdf/17/1/49/6026060/17-1-49.pdf},
+			}
+@article{Zhang:2011,
+	Author = {Zhang, R and Pakhomov, S and McInnes, B T and Melton, G B},
+	Year = {2011},
+	Title = {Evaluating measures of redundancy in clinical texts},
+	Journal = {AMIA Annu Symp Proc},
+	Pages = {1612--1620},
+	Url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243221/}
+	}
+
+@article{Gabriel:2018,
+	Author = {Rodney A. Gabriel and Tsung-Ting Kuo and Julian McAuley and Chun-Nan Hsu},
+	Journal = {Journal of Biomedical Informatics},
+	Year = {2018},
+	Volume = {82},
+	Pages = {63--69}
+}
diff --git a/paper/paper.md b/paper/paper.md
new file mode 100644
index 0000000..ef6d789
--- /dev/null
+++ b/paper/paper.md
@@ -0,0 +1,367 @@
+---
+title: 'Bloatectomy: A python package for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents'
+tags:
+  - python
+  - medical informatics
+  - electronic health records
+  - electronic medical records
+  - public health informatics
+  - clinical information extraction
+  - health informatics
+  - natural language processing
+authors:
+  -  name: Summer K. Rankin*
+     orcid: 0000-0002-6886-3983
+     affiliation: "1"
+  -  name: Roselie Bright
+     orcid: 0000-0002-7565-1284
+     affiliation: "2"
+  -  name: Katherine Dowdy
+     affiliation: "1"
+affiliations:
+ - name: Booz Allen Hamilton, McLean, VA, USA
+   index: "1"
+ - name: Office of Health Informatics, Office of the Chief Scientist, Office of the Commissioner, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, USA 20993-0002 roselie.bright@fda.hhs.gov.
+   index: "2"
+date: 1 June 2020
+bibliography: paper.bib
+---  
+
+*corresponding author
+The authors are listed in order of contributions to the work and manuscript.
+
+
+# Summary
+Objective: Identify duplicate sentences in unstructured healthcare notes, mark them for manual review, and remove them from statistical analysis.
+Introduction: Duplicated sentences (“note bloat”) in unstructured electronic healthcare records hamper scientific research. Existing methods did not meet our needs.
+Methods: We adapted the LZW compression algorithm into a new method and designed parameters to allow customization for varying data and research needs.
+Results: Examples are presented.
+Conclusions: The Bloatectomy method works, is available for use, and can be adapted for other settings.
+
+![Graphical Abstract.  ](graph_abstract.png)
+
+# Introduction
+The authors are part of a team that is using the text notes in electronic healthcare records (EHRs). Our EHRs are a de-identified hospital critical care data set known as the Medical Information Mart for Intensive Care (MIMIC-III)[@mimiciii; @mimiciiidata; @physionet].
+
+Most notes were made by physicians (attending, radiology, consulting) and CCU nurses. Most of these notes included sections that were duplicates of earlier notes (written by themselves or another provider) for the same patient’s hospitalization (their admission). Sometimes pasted sections were edited, and then the modified text was duplicated into later notes (see \autoref{notes1} and \autoref{notes2}).
+
+![Progressively longer nurse’s notes over one shift. In this figure, we used a manual method to highlight identical sentences with unique colors. The original sentences are in bold font as well. This and similar figures in this document are purposely small and low resolution to provide further patient and provider privacy protection without disturbing our point about duplicate text.\label{notes1}](notes1.png)
+
+![Example of two physicians’ notes from the same time period. In this figure, we used a manual method to highlight identical sentences with unique colors. The original sentences are in bold font as well.\label{notes2}](notes2.png)
+
+This type of duplication has been noticed in other health care settings [reviewed in @Dean:2018]. The duplications distort statistical analyses of terms used and hamper manual review of the notes for changes in patient care and status. Removing these duplicated notes allows us to use a wide variety of statistical methods without concern for the weights introduced by duplicates.
+
+For example, if we are using a simple frequency (count) vectorization method, the more times a word appears, the more important it is in the analysis. Furthermore, as length of stay increases, the burden of duplicates also increases, which inflates the importance of, for example, admission comments. Artificial repeats (copies) of text will artificially inflate the importance of the repeated words or phrases [@Cohen:2013]. Though existing methods can weight words (i.e., term-frequency x inverse document frequency [TF-IDF]), for this dataset these methods did not yield satisfactory results.
+
+Our goal was to identify duplicated sections to aid manual review and to delete them from statistical processes. We developed criteria for the tool that were based on the clinical setting and our desire to keep all new clinical information. Specifically:
+
+1.	Minimum clinical concepts were at the sentence or partial or complete list level.  Minor changes within a sentence or list could change the clinical meaning; for example, insertion of the word “not” reverses the meaning of a sentence. We wanted a tool that would find exact duplicate sentences and lists.
+2.	Duplications could occur as an entire note or partial note. We wanted to find both partial and entire duplicates of notes.
+3.	Duplications from more than one original source could occur in a single note, and we wanted to find all of them.
+4.	Exact or partial duplications could occur across many notes, and we wanted to find all of them.
+5.	Lists of clinical parameters and values could be completely or partially duplicated across notes. In addition to finding duplicates of entire lists, we wanted to find duplicates of significantly long sections of lists in the notes.
+6.	Document structure (headings, paragraph formats, list formats) of notes varied widely. We needed a method with broad independence of the internal structures.
+7.	We wanted the method to be simple and user-friendly if doing so resulted in an acceptably low level of error.
+8.	We wanted to be able to use the output for two purposes:
+    a.	Aid manual review of notes by marking the duplicate sections
+    b.	Statistical analyses
+
+
+We evaluated existing available tools and strategies.
+- Plagiarism tools typically compare documents and stop after finding the first instance of duplicate text [@Su:2008; @copyfind; @Ceglarek:2013]; some also appropriately account for paraphrasing. Some other duplication detection methods [@Thielke:2007; @Wrenn:2010] rely on the Bloomfield plagiarism tool. We focused on concepts at the sentence level because word meaning depends on its immediate sentence and paragraph, regardless of where it appears in the note. Genetic and protein sequence comparators also finish after finding similar sequences [@Altschul:1990]. We wanted to find exact duplicates and every instance of them.
+- Some existing EHR deduplication methods have been applied only to the discharge note [@Cohen:2013]. Unfortunately, the discharge notes in MIMIC III are not comprehensive accounts of all observations and treatments that may be key to specific research questions; for example, blood transfusions are sometimes noted in coded data or nursing notes yet were absent from the discharge summary.
+- Fingerprinting identifies redundant entire notes based on similarity [@Cohen:2013], but it finds inexact duplicates (which could be clinically different in some way). Also, we wanted to identify duplicate concepts within notes even if some other parts of the note were original.
+- Clustering entire notes, and then looking for entire and near duplicate pairs of notes within clusters [@Gabriel:2018] works only on entire notes. We wanted to detect duplicate sentences and lists within notes, even if new material existed among the duplicates.
+- Detecting duplicates through topic modeling, an unsupervised and unreplicable [@Lancichinetti:2015] method of clustering related words and expressions within documents, [@Cohen:2014] involved comparing two simplifications that did not fit our needs
+    - Selection and use of a best note within the admission. We found that crucial clinical information was missing from any one best note in an admission.
+    - Selection of the one source note, the longest one, in the admission.  We found that in MIMIC III, there is no single source note in an admission.
+- Sliding windows [@Zhang:2011], similar to frameshifting in other fields, were a possibility if we adapted them to sentences and lists. We believe our method is simpler to implement.
+
+
+The LZW compression algorithm [@Welch:1984] hashes words by assigning sequential numeric addresses to them. Any word that has been hashed before is assigned the original numeric address. We adapted the method to sentences rather than words because the altered sentences in pasted text also had new meanings. The hashing system allowed us to both highlight pasted sentences in text documents and remove the duplicate sentences during preprocessing for statistical analysis.
+
+# Duplicate Detection
+![High-level flowchart of the Bloatectomy method. ](flowchart.png)
+
+## Document Selection
+In Natural Language Processing (NLP), we refer to a unit of text as a document. In the MIMIC-III database, free-text notes from multiple sources are associated with a patient's admission (HADM_ID). For our analysis, a document consisted of the concatenation of all notes for an admission into one single document in chronological order. Thus, there is one document per admission.
+
+## Code and Example
+The Bloatectomy (v.2.1) tool can be found in the python package ``bloatectomy.py``. The MIMIC-III database was stored in a PostgreSQL (v.9.4) database. The Bloatectomy tool was written using only Python 3 (v.3.7.3) [@python3]. No other libraries are needed to identify duplicate text. An option to ingest or output a word document requires the python-docx library.
+The Bloatectomy method is as follows:
+1.	Create sentence tokens.
+2.	Create list tokens.
+3.	Assign either new or old hash number.
+4.	Use the hash table of tokens to:
+    - Mark duplicate tokens in a recreated document for manual review.
+    - Create a string from the unduplicated tokens for statistical analysis.
+
+### Create Sentence Tokens
+First, we tokenized (separated) an admission's concatenated notes (a single document) based on the presence of:
+- a period ( . ) followed by one or more
+    white space characters (space, tab, line breaks) or a
+    line feed character (\n).  
+
+An example of the text seen in an EHR looks like the following:
+```No CP. Became tachycardic to 160s on dopa. No CP.
+Tmax: 36.6
+C (97.8
+HR: 100 (97 - 166) bpm
+Tmax: 36.6
+C (97.8
+```
+
+The plain text—i.e., only the characters used to represent the text—looks like the following:
+```
+No CP. Became tachycardic to 160s on dopa. No CP.\nTmax: 36.6\nC (97.8\nHR: 100 (97 - 166) bpm\nTmax: 36.6\nC (97.8
+```
+The first tokenization was accomplished using a regular expression \autoref{regex1} in python (v.3.7.3) using the `re` (regular expression) library. This regular expression can be changed by passing any valid regular expression for the `regex1` parameter.
+![The regular expression used for the first tokenization.\label{regex1}](regex1.png)
+
+At this point, we have two tokens because there are two periods followed by a line feed character.
+
+| Token Number    | Token     |
+| :------------- | :------------- |
+| 1       | No CP.       |
+| 2       |Became tachycardic to 160s on dopa. |
+| 3 | No CP.       |
+|4 | \nTmax: 36.6\nC (97.8\nHR: 100 (97 - 166) bpm\nTmax: 36.6\nC (97.8 |
+
+
+
+### Create List Token
+Next, each token is examined and split again if it contains a line feed character followed by one or more:
+- Upper case character
+- Digit
+- En dash (-)
+- Number sign (#)  
+
+Using our sample text, token 4 is split into several new tokens, which are then inserted into our token list. If a token does not need to be split further, it is added to our list as-is. The regular expression for the split described in \autoref{regex2}. This regular expression can be changed by passing any valid regular expression for the `regex2` parameter.
+
+![The regular expression used to calculate the second tokenization.\label{regex2}](regex2.png)
+
+After the token has been split, the text is cleaned up to increase the matches and focus on the text rather than the formatting. All line feed characters are replaced with one white space. White spaces at the beginning and end of a token are removed, then the token is added to the new token list.
+
+```
+# replace \n with a space with a space
+sent_token = [re.sub(r"$\n+","",i) for i in sent_token] # remove from end
+sent_token = [re.sub(r"^\n", "", i) for i in sent_token] #remove from front
+
+# line feeds + whitespace or not
+sent_token = [re.sub(r"\s+\n\s+", " ", i) for i in sent_token]
+sent_token = [re.sub(r"\s+\n", " ", i) for i in sent_token]
+sent_token = [re.sub(r"\n\s+", " ", i) for i in sent_token]
+sent_token = [re.sub(r"\n", " ", i) for i in sent_token]
+
+#remove front/end whitespace
+sent_token = [i.strip(' ') for i in sent_token]
+```
+
+Specifically, token 4 becomes 5 new tokens:
+
+
+| Token Number    | Token     |
+| :------------- | :------------- |
+| 1       | No CP.       |
+| 2       |Became tachycardic to 160s on dopa. |
+| 3 | No CP.       |
+|4 | Tmax: 36.6 |
+| 5 | C (97.8 |
+|6 | HR: 100 (97 - 166) bpm |
+|7 | Tmax: 36.6 |
+|8 | C (97.8 |
+
+
+### Assign Either New or Old Hash Number
+Next, we create a dynamic set structure that accepts tokens one at a time. The function is a generator that yields one token at a time; when the output is stored as a list, the original order of tokens is maintained while providing the flexibility to either remove or mark (highlight, bold) a duplicate token.
+As each token is passed through the loop, one of two outcomes will result:
+1.	If the token is not in the dynamic set, it will be added to the set, and the token is yielded as-is.
+2.	If the token is already contained in the set, it is NOT added to the set; HTML tags are added to the token, yielding a marked token. If we want to remove the token, nothing is yielded at this step.
+
+```
+tokens_set = set()
+tokens_set_add = tokens_set.add
+
+for token in input_tokens:
+
+  if token == '':
+    pass
+
+  elif token not in tokens_set:
+    tokens_set_add(token)
+    yield token
+
+  elif remov == False:
+    yield tag + token + tag_end
+
+  elif remov == True:
+    pass
+```
+The token table essentially becomes the following hash table of tokens:
+
+| Token Number    | Token     | Bloat | Hash # |
+| :------------- | :------------- |:------------- |:------------- |
+| 1       | No CP.       | No | 1 |
+| 2       |Became tachycardic to 160s on dopa. | No | 2|
+| 3 | No CP.       | Yes | 1|
+|4 | Tmax: 36.6 | No | 3|
+| 5 | C (97.8 | No | 4 |
+|6 | HR: 100 (97 - 166) bpm | No | 5 |
+|7 | Tmax: 36.6 | Yes | 3 |
+|8 | C (97.8 | Yes | 4 |
+
+
+### Use the Hash Table of Tokens
+The output is a document with a list of our original tokens with highlight formatting marks around the duplicates. The user can choose to highlight, bold, or remove duplicates by setting the ``style`` argument.
+
+```
+import bloatectomy from bloatectomy
+bloatectomy(text, style='highlight'))
+```
+
+| Token Number    | Token     |
+| :------------- | :------------- |
+| 1       | No CP.       |
+| 2       |Became tachycardic to 160s on dopa. |
+|  duplicate | <mark>No CP.</mark>       |
+|4 | Tmax: 36.6 |
+| 5 | C (97.8 |
+|6 | HR: 100 (97 - 166) bpm |
+|duplicate | <mark>Tmax: 36.6</mark> |
+|duplicate | <mark>C (97.8</mark> |
+
+Last, we concatenate this marked (or de-duplicated) set of tokens together to create a document of original sentences. A line feed is placed between each token for ease of viewing (due to removing the line feed characters at the beginning and end of each token). How this is executed depends on the type of output (e.g., docx, HTML).
+```
+uniq = str("\n".join(text))
+```
+The final output contains three highlighted duplicate tokens:
+
+![Marked output for the example text.  ](example_output.png)
+
+The marked text can be deleted for statistical analyses.
+```
+bloatectomy(text, style='remov'))
+```
+
+## Parameter Adjustments
+We incorporated parameters that users can set to fit the features of the documents they are using. The following input types can be used:
+1.	Plain text files (.txt, .rtf)
+2.	Word documents (.docx)
+3.	A variable containing a string of raw text
+4.	A list of HADM_ID values for a PostgreSQL database (specific to MIMIC III database)
+
+The following output types are available:
+1.	HTML file (.html)
+2.	Word document (.docx)
+3.	Print the text to the console
+4.	Numbered tokens from original (raw) text (.txt)
+5.	Numbered tokens after duplication detection (.txt)  
+
+The duplicates can be marked using:
+1. highlighting
+2. bold
+3. remove
+
+
+# Results
+\autoref{output_notes1} and \autoref{output_notes2} show the sample nurse’s and physicians’ notes after Bloatectomy.
+
+![Figure 4. The same nurse’s notes from \autoref{notes1}, after Bloatectomy.\label{output_notes1}](output_notes1.png)
+![Figure 5. The same physicians’ notes from \autoref{notes2}, after Bloatectomy.\label{output_notes2}](output_notes2.png)
+
+We note that Bloatectomy doesn’t exactly replicate manual evaluation of duplications.   
+
+# Conclusions
+We accepted some error in favor of simplicity and our reluctance to over-customize the algorithm. We favored leaving in duplicates over marking new information because of our research goals. Other users will find the amount of error, of either type, depends on the software parameters and the data. At least some manual review of their data will help users choose their own balance of types of errors to accept, depending on their goals.  
+
+Bloatectomy is an effective tool for identifying duplicate text in EHRs and would be useful for other types of documents with multiple instances of duplicate sentences or paragraphs.
+
+# Summary
+Duplicated sentences (“note bloat”) in unstructured electronic healthcare records hamper scientific research.  
+Existing methods did not meet our needs.  
+We adapted the LZW compression algorithm into a new method and designed parameters to allow customization for varying data and research needs.  
+The Bloatectomy method works, is available for use, and can be adapted for other settings.  
+
+# Sample Text
+To run bloatectomy on the sample text, use this in python 3.7.x or higher
+
+```
+from bloatectomy import bloatectomy
+
+bloatectomy('sample_text.txt',
+             filename='sampletxt_output',
+             display=False,
+             style='highlight',
+             output='html',
+             output_numbered_tokens=True,
+             output_original_tokens=True )
+```
+This takes in the single text file (i.e., sample_text.txt) to be marked for duplicates. The marked output, original numbered tokens and marked numbered tokens can be output. Note that the tokens in the two numbered token files will have the same token numbers unless style='remov'.
+
+## Parameters  
+```
+class bloatectomy(input_text,
+                  path = '',
+                  filename='bloatectomized_file',
+                  display=False,
+                  style='highlight',
+                  output='html',
+                  output_numbered_tokens=False,
+                  output_original_tokens=False,
+                  regex1=r"(.+?\.[\s\n]+)",
+                  regex2=r"(?=\n\s*[A-Z1-9#-]+.*)",
+                  postgres_engine=None,
+                  postgres_table=None)
+```
+
+**input_text**: file, str, list  
+An input document (.txt, .rtf, .docx), a string of text, or list of hadm_ids for postgres mimiciii database or the raw text.
+
+**style**: str, optional, default=`highlight`  
+Method for denoting a duplicate. The following are allowed: `highlight`, `bold`, `remov`.
+
+**filename**: str, optional, default=`bloatectomized_file`
+A string to name output file of the bloat-ectomized document.
+
+**path**: str, optional, default=`' '`  
+The directory for output files.
+
+**output_numbered_tokens**: bool, optional, default=`False`  
+If set to `True`, a .txt file with each token enumerated and marked for duplication, is output as `[filename]_token_numbers.txt`. This is useful when diagnosing your own regular expression for tokenization or testing the `remov` option for **style**.
+
+**output_original_tokens**: bool, optional, default=`False`  
+If set to  `True`, a .txt file with each original (non-marked) token enumerated but not marked for duplication, is output as `[filename]_original_token_numbers.txt`.
+
+**display**: bool, optional, default=`False`  
+If set to `True`, the bloatectomized text will display in the console on completion.
+
+**regex1**: str, optional, default=`r"(.+?\.[\s\n]+)"`  
+The regular expression for the first tokenization. Split on a period (.) followed by one or more white space characters (space, tab, line breaks) or a line feed character (\n). This can be replaced with any valid regular expression to change the way tokens are created.
+
+**regex2**: str, optional, default=`r"(?=\n\s*[A-Z1-9#-]+.*)"`  
+The regular expression for the second tokenization. Split on any newline character (\n) followed by an uppercase letter, a number, or a dash. This can be replaced with any valid regular expression to change how sub-tokens are created.
+
+**postgres_engine**: str, optional
+The postgres connection. Only relevant for use with the MIMIC III dataset. See the jupyter notebook [mimic_bloatectomy_example](../bloatectomy_examples/mimic_ bloatectomy_example.ipynb).
+
+**postgres_table**: str, optional
+The name of the postgres table containing the concatenated notes. Only relevant for use with the MIMIC III dataset.
+
+
+
+# Acknowledgements
+The authors thank Walter G. Bright, originator of the D language (WalterBright.com), for suggesting the LZW algorithm. Serge Blok, PhD, of Booz Allen Hamilton, offered the name Bloatectomy and reviewed the draft manuscript. Daily project support was provided by George Plopper, PhD, and Lauren Neal, PhD at Booz Allen Hamilton, and Letria Hall and Elaine Johanson at FDA. Susan Bright, DVM, MPH, and Lee Anne Palmer, VMD, MPH, both in the Office of Surveillance and Compliance, Center for Veterinary Medicine, FDA, participated in establishing the need to identify and remove duplicate text.
+
+# Funding
+This work was supported by the FDA Shakespeare Electronic Health Records (EHR) Text Mining Project HHSF223201510027B.
+
+# Conflicts of Interest
+The authors have declared that no competing interests exist.
+
+# Disclaimer
+The statements made in this article are not necessarily the official policy of the Food and Drug Administration.
+
+# Data
+All data for the development of this tool are located at the MIMIC-III website https://mimic.physionet.org. The code is located at https://github.com/MIT-LCP/mimic-code
+
+# References
diff --git a/paper/regex1.png b/paper/regex1.png
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diff --git a/setup.py b/setup.py
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--- /dev/null
+++ b/setup.py
@@ -0,0 +1,42 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+try:
+    from setuptools import setup
+except ImportError:
+    from distutils.core import setup
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+setup(
+    name="bloatectomy", # Replace with your own username
+    version="0.0.11",
+    author="Summer Rankin, Roselie Bright, Katherine Dowdy",
+    author_email="summerKRankin@gmail.com",
+    description="Bloatectomy: a method for the identification and removal of duplicate text in the bloated notes of electronic health records and other documents.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/MIT-LCP/bloatectomy",
+    packages=['bloatectomy'],
+    license="GPLv3",
+    keywords=["python", "medical informatics","electronic health records",
+    "electronic medical records", "public health informatics",
+    "clinical information extraction", "informatics", "natural language processing"],
+    classifiers=[
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 3 :: Only",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
+        "Operating System :: OS Independent",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Science/Research",
+        "Topic :: Scientific/Engineering",
+        "Topic :: Scientific/Engineering :: Information Analysis",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+        "Topic :: Text Processing",
+        "Topic :: Utilities",
+    ],
+    python_requires='>=3.7',
+)