  Rich Electronic Health Records (EHR), have created opportunities to improve
clinical processes using machine learning methods. Prediction of the same
patient events at different time horizons can have very different applications
and interpretations; however, limited number of events in each potential time
window hurts the effectiveness of conventional machine learning algorithms. We
propose a novel time associated meta learning (TAML) method to make effective
predictions at multiple future time points. We view time-associated disease
prediction as classification tasks at multiple time points. Such
closely-related classification tasks are an excellent candidate for model-based
meta learning. To address the sparsity problem after task splitting, TAML
employs a temporal information sharing strategy to augment the number of
positive samples and include the prediction of related phenotypes or events in
the meta-training phase. We demonstrate the effectiveness of TAML on multiple
clinical datasets, where it consistently outperforms a range of strong
baselines. We also develop a MetaEHR package for implementing both
time-associated and time-independent few-shot prediction on EHR data.
