建模疾病进展中的健康不平等，避免低估弱势群体病情。
Learning Disease Progression Models That Capture Health Disparities
- 基于贝叶斯框架，捕捉三类健康不平等：就诊延迟、进展加速、随访减少。
- 在心衰患者数据上验证，忽略不平等会导致对弱势群体病情严重度低估。
- 适合关注医疗公平性与临床风险评估的研究者使用。
疾病进展模型广泛用于多种慢性病的诊疗决策。然而，现有模型未考虑可能扭曲观测数据的健康不平等。为此，我们提出一种可解释的贝叶斯疾病进展模型，能够捕捉三类关键健康不平等现象：（1）某些患者群体仅在疾病更严重时才开始接受治疗；（2）即使接受治疗，其疾病进展速度仍更快；（3）随访频率随病情严重程度降低。理论与实证均表明，忽略这些不平等会导致病情严重度估计偏差（如低估弱势群体）。在心衰患者数据集上，模型成功识别出面临不同类型不平等的群体，且在推断病情严重度时，考虑不平等显著改变了高风险患者的判定结果。
原文摘要 · Abstract (English)
Disease progression models are widely used to inform the diagnosis and treatment of many progressive diseases. However, a significant limitation of existing models is that they do not account for health disparities that can bias the observed data. To address this, we develop an interpretable Bayesian disease progression model that captures three key health disparities: certain patient populations may (1) start receiving care only when their disease is more severe, (2) experience faster disease progression even while receiving care, or (3) receive follow-up care less frequently conditional on disease severity. We show theoretically and empirically that failing to account for any of these disparities can result in biased estimates of severity (e.g., underestimating severity for disadvantaged groups). On a dataset of heart failure patients, we show that our model can identify groups that face each type of health disparity, and that accounting for these disparities while inferring disease severity meaningfully shifts which patients are considered high-risk.
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