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Primer Reading and Video List
jonc101 edited this page Oct 2, 2019
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Collection of some primer reading and videos to spark some ideas of things to work on. (Besides of course the group's own publications and videos.)
- Michael Lauer - Transforming Epidemiology and Clinical Trials in an Era of Big Data and Small Budgets Surprisingly inspirational review of state of medical research and the need for experts trained in data sciences.
- Marc Cullen - RCT's Will Never Provide Enough - In Defense of Observational Evidence Good review of interplay between value of randomized controlled trials and the relevance of observational studies.
- Eric Strong - Why Most Published Research is Wrong Overview from a practicing clinician and educator's perspective, trying to navigate clinical decisions in the context of confusing and conflicting research.
- Leonard D'AVolio - Big Data and Machine Learning in Healthcare: How, Why, and When Overview of potential applications in healthcare with perspective on policy and economic drivers that will override technology solutions.
- PBS - Sick Around America Older video just before ACA was passed, but paints a compelling (if grim) picture of how nasty the problems in healthcare can be.
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Best Care at Lower Cost - The Path to Continuously Learning Health Care in America In particular, I very much enjoyed Chapter 6 - Generating and Applying Knowledge in Real Time, that motivated much of my early work (very helpful for building Intros and Motivation for papers and proposals).
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Is There a High Degree of Scientific Certainty in Modern Medicine? Surprised me over the course of medical training how most of what we do in medicine is based on somewhat arbitrary best guesses.
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Google Crash Course on Framing Machine Learning Problems Not a technical review, but a very nice overview on how to frame problems that are (or are NOT) good to approach with machine learning.
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Demystifying AI in Healthcare Pretty good industry report breaking down some of the terminology and credible application dependencies.
(I have copies of most of these, if you're interested in browsing)
- Atul Gawande - The Case of the Red Leg I don't know if there's a free version available, but this particular chapter is a riveting account of some very messy, high-stakes decisions to be made in medicine, with a cursory overview of decision analysis.
- Atul Gawande - Cowboys and Pit Crews Perspective on the escalating complexity of medicine and need for disciplines from engineering and informatics to public health and economics to make modern healthcare work. I often use this material for my paper / proposal Intros.
- Vinay Prasad, Adam Cifu - Ending Medical Reversal Medical provocateurs writing about clinical evidence and how much of what we do is proven wrong (and yet we can't stop doing it.)
- Elisabeth Rosenthal - An American Sickness - How Healthcare Became Big Business and How You Can Take It Back Broad overview of so many financial distortions in the healthcare system. Should make you angry. Hopefully enough to channel that towards contributing to making things better. She also wrote a whole series in the New York Times on "Paying Till it Hurts" with many of the same themes.
- Machine Learning and Evidence-Based Medicine Brief perspective comparing and contrasting machine learning approaches vs. evidence-based medicine methodology that is much more firmly established in clinical practice and research.
- John Brush - The Science and the Art of Medicine Simple read on different decision making concepts and evidence based medicine interpretation that doctors should (but often don't) understand.
- Before Disrupting Healthcare Simple read summarizing basic healthcare and information technology terms and trends as a starting point to get familiar with the landscape.
- Be Irv Weissman - "I don't bother keeping up with the literature, because I don't want my mind polluted by other people's bad ideas... If someone else does have a good idea, by reading about it, at best I can only be the second person to come up with it."
- If you're not Irv Weissman, recognize that it is hopeless, and that you can never read enough anyway. Don't Read This Article.
- Alerts - Use Google Scholar or similar indexing services to search for keywords or people of interest to you. You can then "subscribe" to those keywords and Google Scholar will email you every week with any new publications that match those keyword searches.
- Subscriptions - Identify the top journals/venues you're interested (the places you'd want to publish in) and subscribe to have their Table of Contents emailed to you regularly.
- Citation Manager (e.g., PaperPile, Mendeley, etc.) - If you even look at the abstract of a paper, always add it to your citation manager. Will help you track things down when you want to review them again later.
- Peer Review - Be willing to peer review at least one paper for every paper you submit. You'll learn more about the publication process, what issues will kill a paper submission, and naturally see what other people in the field are trying to work on.
- Social Media - Get on Twitter and start following people of interest who post and comment about the kinds of articles you care about. Then read those papers or abstracts to get involved in the discussions.
- Quantity - If you average one abstract per day and two full length articles per week, you should be in decent shape. This assumes you are getting an in depth understanding of the methods used. If you don't understand the methods or background well, the best learning is when you keep following the reference trail to understand everything.