The next decade will see a shift in focus of machine learning in healthcare from models for diagnosis and prognosis to models that directly guide treatment decisions. We introduce methods for learning treatment policies from electronic medical records, and demonstrate their use in learning to recommend antibiotics for women with uncomplicated urinary tract infections. Our methods can take into consideration multiple factors, e.g. efficacy, cost, risk of complications, that should be optimized when learning policies. We show how to perform policy distillation, after learning, to simplify deployments. We introduce the concept of a 'target deployment' to guide retrospective evaluation, showing how this can be used to obtain fair comparisons to existing clinical practice. We find that, relative to clinicians, our models reduce inappropriate antibiotic prescriptions from 11.9% to 9.5% while at the same time using 50% fewer second-line antibiotics. Finally, we discuss mistakes that we made and lessons learned. Based on joint work with Sooraj Boominathan, Michael Oberst, Helen Zhou, and Sanjat Kanjilal (BWH/MGH).
Going beyond diagnosis and prognosis: Machine learning to guide treatment suggestions
September 23, 2020
Dept. of Electrical Engineering and Computer Science, Institute for Medical Engineering & Science, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology