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. To address these challenges, we propose a novel hybrid global-local architecture and a pharmacokinetic encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains for a blood glucose forecasting task using both realistically simulated and real-world data. Our global-local architecture improves over patient-specific models by 9.2-14.6%. Additionally, our pharmacokinetic encoder improves over alternative encoding techniques by 4.4% on simulated data and 2.1% 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.

翻译：医疗时间序列预测对于不良预后的早期检测和患者监测至关重要。然而，由于数据噪声和间歇性，实际预测往往面临困难。外部因素（如药物干预）导致的变点进一步加剧了这些挑战。为应对这些问题，我们提出一种新型混合全局-局部架构及药代动力学编码器，该编码器能够向深度学习模型传递患者特异性治疗效果信息。我们通过模拟真实场景与真实世界数据，在血糖预测任务中验证了该方法在显著提升预测精度方面的有效性。全局-局部架构相较于患者特异性模型取得了9.2%-14.6%的性能提升。此外，我们的药代动力学编码器在模拟数据和真实数据上分别比替代编码技术提升了4.4%和2.1%。所提出的方法在临床实践中具有多重有益应用，例如可对非预期治疗反应发出早期预警，或帮助基于药物吸收与消除特性刻画患者特异性治疗效果。