Time Associated Meta Learning for Clinical Prediction
This addresses the challenge of sparse data in clinical prediction for healthcare applications, though it is incremental as it builds on existing meta-learning approaches.
The paper tackles the problem of predicting patient events at multiple future time points from EHR data, where sparse events per time window limit conventional methods, and proposes a time-associated meta learning (TAML) method that consistently outperforms strong baselines on clinical datasets.
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.