Patient trajectories from electronic health records are widely used to predict potential outcomes 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 predict potential outcomes 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 prediction of the potential outcomes. 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.

翻译：电子健康记录中的患者轨迹被广泛用于预测随时间推移的治疗潜在结果，进而实现个性化医疗。然而，现有用于此目的的神经方法存在一个关键局限：尽管部分方法能够校正时变混杂因素，但这些方法均假设时间序列是在离散时间记录的。换言之，它们受限于测量和治疗在固定时间步进行的设定，而这在医疗实践中并不符合实际情况。本研究旨在实现连续时间下的潜在结果预测。连续时间建模具有直接的实际意义，因为它能够对测量和治疗发生在任意不规则时间戳的患者轨迹进行建模。为此，我们提出了一种名为稳定连续时间逆倾向网络（SCIP-Net）的新方法。在此基础上，我们进一步推导出稳定逆倾向权重，以实现潜在结果的稳健预测。据我们所知，SCIP-Net是首个能在连续时间框架下对时变混杂因素进行恰当校正的神经方法。