  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.
