提出新方法实现连续时间下的治疗效果预测，更贴近真实医疗数据
Stabilized Neural Prediction of Potential Outcomes in Continuous Time
- 设计SCIP-Net模型，支持在任意时间点进行治疗效果估计
- 引入稳定逆概率权重，有效应对随时间变化的混杂因素
- 适用于测量和治疗时间不规则的临床场景，适合医疗个性化决策
电子健康记录中的患者轨迹被广泛用于估计治疗在时间上的条件平均潜在结果（CAPO），以实现个性化医疗。然而，现有神经方法存在关键局限：尽管部分方法能调整随时间变化的混杂因素，但均假设时间序列以离散时间点记录，即要求测量和治疗在固定时间间隔进行，这在实际医疗中并不现实。本文旨在实现连续时间下的CAPO估计，具有直接实践意义——允许建模测量和治疗发生在任意、不规则时间戳的患者轨迹。为此，我们提出一种新方法：稳定连续时间逆概率网络（SCIP-Net），并推导出用于鲁棒估计的稳定逆概率权重。据我们所知，SCIP-Net是首个在连续时间下正确调整随时间变化混杂因素的神经方法。
原文摘要 · Abstract (English)
Patient trajectories from electronic health records are widely used to estimate conditional average potential outcomes (CAPOs) of treatments over time, which then allows to personalize care. Yet, existing neural methods for this purpose have a key limitation: while some adjust for time-varying confounding, these methods assume that the time series are recorded in discrete time. In other words, they are constrained to settings where measurements and treatments are conducted at fixed time steps, even though this is unrealistic in medical practice. In this work, we aim to estimate CAPOs in continuous time. The latter is of direct practical relevance because it allows for modeling patient trajectories where measurements and treatments take place at arbitrary, irregular timestamps. We thus propose a new method called stabilized continuous time inverse propensity network (SCIP-Net). For this, we further derive stabilized inverse propensity weights for robust estimation of the CAPOs. To the best of our knowledge, our SCIP-Net is the first neural method that performs proper adjustments for time-varying confounding in continuous time.
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