Forecasting Response to Treatment with Deep Learning and Pharmacokinetic Priors

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.