# Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis

arXiv: https://arxiv.org/abs/2411.10645 · HTML: https://mlsanity.com/paper/2411.10645/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [Learning Sample-Specific Models with Low-Rank Personalized Regression](https://mlsanity.com/paper/1910.06939/) (2019)
- [Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning](https://mlsanity.com/paper/2310.07918/) (2023)
- [Contextualized Machine Learning](https://mlsanity.com/paper/2310.11340/) (2023)
- [Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer](https://mlsanity.com/paper/2101.09587/) (2021)

## Abstract (arXiv)

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.