Personalized Medication Dosing Using Volatile Data Streams

Ghassemi, Mohammad M. (Massachusetts Institute of Technology) | AlHanai, Tuka (Massachusetts Institute of Technology) | Westover, M. Brandon (Massachusetts General Hospital) | Mark, Roger G. (Massachusetts Institute of Technology) | Nemati, Shamim (Emory University)

AAAI Conferences 

One area of medicine that could benefit from personalized procedures is medication dosing. Mis-dosing medications may incur additional morbidity, or unnecessarily increase the length of patient stay. Here we illustrate a novel approach to personalized medication dosing that is robust to missing data, a common problem in the clinical care setting. We perform dose estimation using a novel take on multinomial logistic regression where model parameters are continuously estimated, for each patient, using a weighted combination of the data from a population of other patients, and a volatile data stream available from the individual under treatment. We evaluate our approach on 4,470 patients who received anti-coagulation therapy during intensive care treatment. Our approach was 29% more accurate than intensive care staff, and better able to distinguish outcomes than a non-personalized baseline (0.11 improvement in model VUS, a multiclass version of AUC). The advantages of our approach are its ease of interpretation, robustness to missing features, and extensibility to other problems with similar structure.