1 research outputs found
Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data
Digital Adherence Technologies (DATs) are an increasingly popular method for
verifying patient adherence to many medications. We analyze data from one city
served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB)
treatment in India where nearly 3 million people are afflicted with the disease
each year. The data contains nearly 17,000 patients and 2.1M dose records. We
lay the groundwork for learning from this real-world data, including a method
for avoiding the effects of unobserved interventions in training data used for
machine learning. We then construct a deep learning model, demonstrate its
interpretability, and show how it can be adapted and trained in different
clinical scenarios to better target and improve patient care. In the real-time
risk prediction setting our model could be used to proactively intervene with
21% more patients and before 76% more missed doses than current heuristic
baselines. For outcome prediction, our model performs 40% better than baseline
methods, allowing cities to target more resources to clinics with a heavier
burden of patients at risk of failure. Finally, we present a case study
demonstrating how our model can be trained in an end-to-end decision focused
learning setting to achieve 15% better solution quality in an example decision
problem faced by health workers.Comment: 10 pages, 6 figure