Predicting Treatment Adherence of Tuberculosis Patients at Scale

• URL: http://arxiv.org/abs/2211.02943v1
• Date: Sat, 5 Nov 2022 17:00:21 GMT
• Title: Predicting Treatment Adherence of Tuberculosis Patients at Scale
• Authors: Mihir Kulkarni, Satvik Golechha, Rishi Raj, Jithin Sreedharan, Ankit Bhardwaj, Santanu Rathod, Bhavin Vadera, Jayakrishna Kurada, Sanjay Mattoo, Rajendra Joshi, Kirankumar Rade, Alpan Raval
• Abstract summary: Non-adherence to TB medication is a significant cause of mortality and morbidity. We formulate and solve the machine learning problem of early prediction of non-adherence based on a custom rank-based metric. Our findings indicate that risk stratification of non-adherent patients is a viable, deployable-at-scale ML solution.
• Score: 0.6873562466909032
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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