@inproceedings{killian2019learning,
author = {Killian, Jackson and Wilder, Bryan and Sharma, Amit and Choudhary, Vinod and Dilkina, Bistra and Tambe, Milind},
title = {Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data},
organization = {ACM},
booktitle = {ACM SIGKDD International Conference on Knowledge Discovery & Data Mining},
year = {2019},
month = {August},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/learning-to-prescribe-interventions-for-tuberculosis-patients-using-digital-adherence-data/},
}