  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.
