Forecasting healthcare time series is crucial for early detection of adverse outcomes and for patient monitoring. Forecasting, however, can be difficult in practice due to noisy and intermittent data. The challenges are often exacerbated by change points induced via extrinsic factors, such as the administration of medication. We propose a novel encoder that informs deep learning models of the pharmacokinetic effects of drugs to allow for accurate forecasting of time series affected by treatment. We showcase the effectiveness of our approach in a task to forecast blood glucose using both realistically simulated and real-world data. Our pharmacokinetic encoder helps deep learning models surpass baselines by approximately 11% on simulated data and 8% on real-world data. The proposed approach can have multiple beneficial applications in clinical practice, such as issuing early warnings about unexpected treatment responses, or helping to characterize patient-specific treatment effects in terms of drug absorption and elimination characteristics.

通过药物的药代动力学效应，我们提出了一种新的编码器，用于准确预测受治疗影响的时间序列，展示了该方法在预测血糖方面的有效性，为临床实践提供多种有益应用。

基于深度学习和药物动力学先验的治疗反应预测