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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