针对医疗决策流程中结局数据缺失问题，提出贝叶斯模型提升风险预测准确性。
A Bayesian Model for Multi-stage Censoring
- 基于贝叶斯框架建模多阶段筛选流程中的选择性删失机制
- 在合成数据中准确恢复真实参数，对删失患者预测更精准
- 发现女性入院重症监护的死亡风险阈值（5.1%）高于男性（4.5%）
医疗中的许多序列决策场景具有漏斗结构，如筛查或评估流程，患者数量逐级减少且决策成本递增。例如，乳腺检查后若结果异常则进行钼靶检查，再对异常者进行活检。关键挑战在于，最终结局（如活检结果）仅在流程末尾才可获得，导致结局数据存在选择性删失，尤其在少数群体中更为严重，可能引入统计偏差。本文提出一种针对漏斗型决策结构的贝叶斯模型，借鉴选择性标签与删失的相关研究。在合成数据中验证该模型能更准确地恢复真实参数并预测删失患者的结局。随后将其应用于急诊科就诊数据，其中住院死亡率仅对入院或转入ICU的患者可观测。结果显示性别间存在差异：女性进入ICU的死亡风险阈值为5.1%，高于男性的4.5%。
原文摘要 · Abstract (English)
Many sequential decision settings in healthcare feature funnel structures characterized by a series of stages, such as screenings or evaluations, where the number of patients who advance to each stage progressively decreases and decisions become increasingly costly. For example, an oncologist may first conduct a breast exam, followed by a mammogram for patients with concerning exams, followed by a biopsy for patients with concerning mammograms. A key challenge is that the ground truth outcome, such as the biopsy result, is only revealed at the end of this funnel. The selective censoring of the ground truth can introduce statistical biases in risk estimation, especially in underserved patient groups, whose outcomes are more frequently censored. We develop a Bayesian model for funnel decision structures, drawing from prior work on selective labels and censoring. We first show in synthetic settings that our model is able to recover the true parameters and predict outcomes for censored patients more accurately than baselines. We then apply our model to a dataset of emergency department visits, where in-hospital mortality is observed only for those who are admitted to either the hospital or ICU. We find that there are gender-based differences in hospital and ICU admissions. In particular, our model estimates that the mortality risk threshold to admit women to the ICU is higher for women (5.1%) than for men (4.5%).
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