Predicting Treatment Adherence of Tuberculosis Patients at Scale

• Published in: arXiv.org 2022-11
• Main Authors: Kulkarni, Mihir, Golechha, Satvik, Rishi Raj, Sreedharan, Jithin, Bhardwaj, Ankit, Rathod, Santanu, Vadera, Bhavin, Kurada, Jayakrishna, Mattoo, Sanjay, Joshi, Rajendra, Rade, Kirankumar, Raval, Alpan
• Format: Article
• Language: English
• Subjects: Bacterial diseases; Machine learning; Tuberculosis
• ISSN: 2331-8422

Tuberculosis (TB), an infectious bacterial disease, is a significant cause of death, especially in low-income countries, with an estimated ten million new cases reported globally in \(2020\). While TB is treatable, non-adherence to the medication regimen is a significant cause of morbidity and mortality. Thus, proactively identifying patients at risk of dropping off their medication regimen enables corrective measures to mitigate adverse outcomes. Using a proxy measure of extreme non-adherence and a dataset of nearly \(700,000\) patients from four states in India, we formulate and solve the machine learning (ML) problem of early prediction of non-adherence based on a custom rank-based metric. We train ML models and evaluate against baselines, achieving a \(\sim 100\%\) lift over rule-based baselines and \(\sim 214\%\) over a random classifier, taking into account country-wide large-scale future deployment. We deal with various issues in the process, including data quality, high-cardinality categorical data, low target prevalence, distribution shift, variation across cohorts, algorithmic fairness, and the need for robustness and explainability. Our findings indicate that risk stratification of non-adherent patients is a viable, deployable-at-scale ML solution. As the official AI partner of India's Central TB Division, we are working on multiple city and state-level pilots with the goal of pan-India deployment.