@conference {1499312,
	title = {Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data},
	booktitle = {The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019},
	year = {2019},
	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 wherenearly 3 million people are afflicted with the disease each year. Thedata contains nearly 17,000 patients and 2.1M dose records. We laythe groundwork for learning from this real-world data, includinga method for avoiding the effects of unobserved interventions intraining data used for machine learning. We then construct a deeplearning model, demonstrate its interpretability, and show how itcan be adapted and trained in three different clinical scenarios tobetter target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with21\% more patients and before 76\% more missed doses than currentheuristic baselines. For outcome prediction, our model performs40\% better than baseline methods, allowing cities to target moreresources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our modelcan be trained in an end-to-end decision focused learning setting toachieve 15\% better solution quality in an example decision problemfaced by health workers.},
	author = {Jackson Killian and Bryan Wilder and Amit Sharma and Vinod Choudhary and Dilkina, Bistra and Tambe, Milind}
}
