GenHPF : General Healthcare Predictive Framework for Multi-task Multi-source Learning

GenHPF is a general healthcare predictive framework, which requires no medical domain knowledge and minimal preprocessing for multiple prediction tasks.

Our framework presents a method for embedding any form of EHR systems for prediction tasks without requiring domain-knowledge-based pre-processing, such as medical code mapping and feature selection.

This repository provides official Pytorch code to implement GenHPF, a general healthcare predictive framework.

Getting started with GenHPF

STEP 1 : Installation

Requirements

• PyTorch version >= 1.9.1
• Python version >= 3.8

STEP 2: Prepare training data

First, download the dataset from these links: MIMIC-III MIMIC-IV eICU ccs_multi_dx_tool_2015 icd10cmtoicd9gem

!

Note that you will need to get access for each dataset, publicly available electronic health records.

Second, for preparation your data, make directory structure like below:

dataset_path
├─ mimic3
│  ├─ ADMISSIONS.csv
│  ├─ PATIENTS.csv
│  ├─ ICUSYAYS.csv
│  ├─ LABEVENTES.csv
│  ├─ PRESCRIPTIONS.csv
│  ├─ PROCEDURES.csv
│  ├─ INPUTEVENTS_CV.csv
│  ├─ INPUTEVENTS_MV.csv
│  ├─ D_ITEMDS.csv
│  ├─ D_ICD_PROCEDURES.csv
│  └─ D_LABITEMS.csv
├─ eicu
│  ├─ diagnosis.csv
│  ├─ infusionDrug.csv
│  ├─ lab.csv
│  ├─ medication.csv
│  └─ patient.csv
├─ mimci4
│  ├─ admissions.csv
│  ├─ …
│  └─ d_labitems.csv
├─ ccs_multi_dx_tool_2015.csv
└─ icd10cmtoicd9gem.csv

Then run preprocessing code

$ bash preprocess/preprocess_run.sh 
    $destination_directory
    $data_directory 

Note that pre-processing takes about 6hours in 128 cores of AMD EPYC 7502 32-Core Processor, and requires 180GB of RAM.

STEP 3. Training a new model

Other configurations will set to be default, which were used in the GenHPF paper. $data should be set to 'mimic3' or 'eicu' or ‘mimic4’ $model should be set to one of [‘SAnD’, ‘Rajkomar’, ‘DescEmb’, ‘GenHPF’]

$task can be set to one of [mortality, long_term_mortality, los_3day, los_7day, readmission, final_acuity, imminent_discharge, diagnosis, creatinine, bilirubin, platelets, wbc] or multiple task. The default setting is multi-task on all of tasks.

Note that --input-path should be the root directory containing preprocessed data.

Example

Train a new GenHPF model:

$ CUDA_VISIBLE_DEVICES=1 \
    python main.py \
    --input_path /path/to/data \
    --model_run GenHPF \
    --train_task scratch \
    --train_src $data \
    --pred_task $pred_task \
    --criterion prediction \
    --batch_size $batch_size \
    --world_size $world_size \

Note: if you want to train with baselines, set model_run as baseline model, one of (Rajikomar, DescEmb, SAnd).

Pre-train GenHPF model:

$ CUDA_VISIBLE_DEVICES=1 \
    python main.py \
    --input_path /path/to/data \
    --model_run GenHPF \
    --model GenHPF_simclr \
    --train_src $data \
    --train_task pretrain  \
    --pretrain_task $pretrain_task \
    --criterion $criterion \
    --batch_size $batch_size \
    --world_size $world_size \
    --valid_subset "" \
    

Note: if you want to train with pre-trained model, add command line parameters --load_checkpoint with directory of pre-trained model checkpoint and set train_task as finetune.

Pooled learning

$ CUDA_VISIBLE_DEVICES=1 \
    python main.py \
    --input_path /path/to/data \
    --model_run GenHPF \
    --train_task scratch \
    --train_src mimiciii_eicu_mimiciv \
    --pred_task $pred_task \
    --criterion prediction \
    --batch_size $batch_size \
    --world_size $world_size \

Note: Please refer main.py argument ('train_src') for pooled learning.

Transfer learning

$ CUDA_VISIBLE_DEVICES=1 \
    python main.py \
    --input_path /path/to/data \
    --model GenHPF \
    –ratio 0 \
    --train_src mimiciii \
    --target_data eicu \
    --train_task finetune  \
    --pred_task $pred_task \
    --criterion prediction \
    --pretrain $scratch \
    --batch_size $batch_size \
    --world_size $world_size \
    --load_checkpoint $saved_ckpt \

Note that --ratio indicates proportion of target dataset for few-shot learning settings. (if ratio is set to zero, then it is zero shot learning)

Citation

If you find GenHPF useful for your research and applications, please cite using this BibTeX:

@article{hur2023genhpf,
  title={GenHPF: General Healthcare Predictive Framework for Multi-task Multi-source Learning},
  author={Hur, Kyunghoon and Oh, Jungwoo and Kim, Junu and Kim, Jiyoun and Lee, Min Jae and Cho, Eunbyeol and Moon, Seong-Eun and Kim, Young-Hak and Atallah, Louis and Choi, Edward},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2023},
  publisher={IEEE}
}

License

This repository is MIT-lincensed.