Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis

• URL: http://arxiv.org/abs/2411.10645v1
• Date: Sat, 16 Nov 2024 00:55:24 GMT
• Title: Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis
• Authors: Ethan Wu, Caleb Ellington, Ben Lengerich, Eric P. Xing,
• Abstract summary: Tuberculosis (TB) is a major global health challenge, compounded by co-morbidities such as HIV, diabetes, and anemia. Traditional models of TB often overlook this heterogeneity by focusing on broad, pre-defined patient groups. We propose moving beyond coarse subgroup analyses by using contextualized modeling, a multi-task learning approach.
• Score: 41.015496177711334
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• Abstract: Tuberculosis (TB) is a major global health challenge, and is compounded by co-morbidities such as HIV, diabetes, and anemia, which complicate treatment outcomes and contribute to heterogeneous patient responses. Traditional models of TB often overlook this heterogeneity by focusing on broad, pre-defined patient groups, thereby missing the nuanced effects of individual patient contexts. We propose moving beyond coarse subgroup analyses by using contextualized modeling, a multi-task learning approach that encodes patient context into personalized models of treatment effects, revealing patient-specific treatment benefits. Applied to the TB Portals dataset with multi-modal measurements for over 3,000 TB patients, our model reveals structured interactions between co-morbidities, treatments, and patient outcomes, identifying anemia, age of onset, and HIV as influential for treatment efficacy. By enhancing predictive accuracy in heterogeneous populations and providing patient-specific insights, contextualized models promise to enable new approaches to personalized treatment.

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