用个性化模型揭示结核病治疗效果差异，精准识别影响疗效的关键因素。
Patient-Specific Models of Treatment Effects Explain Heterogeneity in Tuberculosis
- 基于多任务学习构建患者上下文感知的个性化治疗模型
- 在3000+患者数据中发现贫血、年龄与艾滋病是关键影响因素
- 适合关注结核病个体化治疗与临床决策支持的研究者
结核病（TB）是重大全球健康挑战，常伴发艾滋病、糖尿病和贫血等共病，导致治疗结果复杂化及患者反应异质性。传统TB模型多聚焦于预定义的大群体，忽略个体情境差异。本文提出上下文建模方法，通过多任务学习将患者特征编码为个性化治疗效果模型，揭示患者特异性治疗收益。在包含3000多名患者的TB Portals数据集上应用，模型识别出贫血、发病年龄和艾滋病对治疗效果具有显著影响，揭示了共病、治疗与预后间的结构化交互关系。该模型提升了异质人群预测精度，并提供个体化洞察，有望推动个性化治疗新范式。
原文摘要 · Abstract (English)
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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