Forecasting Treatment Response with Deep Pharmacokinetic Encoders.

Forecasting healthcare time series data is vital for early detection of adverse outcomes and patient monitoring. However, forecasting is challenging in practice due to variable medication administration and unique pharmacokinetic (PK) properties for each patient. To address these challenges, we propose a novel hybrid global-local architecture and a PK encoder that informs deep learning models of patient-specific treatment effects.