Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

Abstract:

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-callbased 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 three 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.