Tag: Data Science
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Introducing Darth Vecdor: A Free, Open-Source Platform to Create Knowledge Graphs Using LLMs (such as ChatGPT)
by Jonathan A. Handler, MD, FACEP, FAMIA I Wanted a Comprehensive Medical Knowledge Graph For decades, I have hypothesized that a comprehensive medical knowledge database (aka, “knowledge base” or “knowledge graph”) would enable radical positive transformation in healthcare. I wanted a database containing relationships between concepts, like: With such a database, I imagined we could…
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The WXP: My “Secret Weapon” Metric in Process Performance Analysis
by Jonathan A. Handler, MD, FACEP, FAMIA Introduction Throughout my professional life, I have performed analyses to assess performance related to some process or function. Whether it’s the performance of people processes, machine processes, or something else, I have found a simple metric (or set of similar metrics) that seems very often to tell me…
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Migrating a Postgres.app database to a new major database version on MacOS
by Jonathan A. Handler, MD, FACEP, FAMIA WARNINGS AND CAVEATS This post was mostly just for me, to remember what I did when it’s time for the next major version upgrade or server move. I’m just sharing my experiences here. However, if it ends up helping someone else, great! My Requirements I took the approach…
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A contradiction in terms: AI interoperability in healthcare
Most AI models can and will only ever be used at the institution(s) at which they were developed. The concept that an AI model is “shareable” — that a model developed at one place can be used at another — is generally a myth. This post explains why, and how the situation can be improved.
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Avoiding False Alerts: Snoozing ≠ Laziness
A simple approach can dramatically reduce false positives and annoying, redundant true negatives. Unfortunately, classic count-based metrics usually lead to the false conclusion that the approach wasn’t helpful! Our simple, novel approach solves that problem, enabling implementations with dramatically fewer false and useless alarms.
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Shocking new discovery: Recall and Sensitivity are not the same!
Classic statistics like sensitivity and specificity make assumptions that are usually false. That leads to serious problems. Our simple, novel approach provides the solution. Imagine this: My personal library contains 100 books, 50 with red bindings and 50 with blue bindings. I hide coins inside 20 of the books. 10 of the red books each…
Zero Effectors 