We still don’t fundamentally understand what it means to be healthy, and the same patient may receive different treatments across different hospitals or clinicians as new evidence is discovered, or individual illness is interpreted.
Unlike many problems in machine learning – games like Go, self-driving cars, object recognition – disease management does not have well-defined rewards that can be used to learn rules. Models must also work to not learn biased rules or recommendations that harm minorities or minoritized populations.
These projects tackle the many novel technical opportunities for machine learning in health, and work to make important progress in health and health equity.
The cross-disciplinary healthcare team includes Kenrick Cato, a nurse researcher for New York Presbyterian Hospital and Columbia School of Nursing professor, and Charles Senteio, professor of library and information science at Rutgers.
