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.

Duplicate Docs Excel Report

Title
None found

Similar Docs  Excel Report  more

TitleSimilarity
None found