# Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data

## TL;DR

This paper develops a deep learning approach using digital adherence data to predict and improve tuberculosis treatment adherence, enabling proactive interventions and better resource allocation in real-world settings.

## Contribution

It introduces a novel method to learn from real-world adherence data, including handling unobserved interventions, and demonstrates its effectiveness in predicting outcomes and guiding interventions.

## Key findings

- Model predicts 21% more at-risk patients proactively.
- Performs 40% better than baseline in outcome prediction.
- Achieves 15% improvement in decision quality in case study.

## 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-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.