Episode 1: Machine Learning Interpretation

Episode 1: Machine Learning Interpretation

In this second episode, we  open with a discussion topic on machine learning model interpretation. We discuss a review paper on mobile health and feature a special segment by Dr. Ben Glicksberg on his open-source ROMOP R package. In our training segment, we discuss impostor syndrome. We also cover some news items and preview a new workshop and conference. Below are links to the websites we mentioned during the episode.

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Here is the LIME paper for machine learning interpretation

Here is the interestingness paper by Geng and Hamilton

IBM Watson in Switzerland


Fastest growing undergraduate major in U.S. is computer science

University of Wisconsin creates new School of Computer, Data, and Information Science

Do ransomware attacks lead to increased risk for MI? News on this from PBS.

Journal club paper on mobile health by Dr. Ida Sim

We discussed the UK Biobank in our open data segment. Here are the PheWAS tools for codes that Marylyn mentioned:




The new COMP2CLINIC workshop at BIOSTEC 2020

The new Symposium on Artificial Intelligence for Learning Health Systems (SAIL) conference

Harvard Business Review piece on Impostor Syndrome