CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction

Understand

Medical event prediction (MEP) is a fundamental task in the medical domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to historical medical records.

• The task is challenging as medical data is a type of complex time series data with heterogeneous and temporal irregular characteristics.
• Many machine learning methods that consider the two characteristics have been proposed for medical event prediction.
• However, most of them consider the two characteristics separately and ignore the correlations among different types of medical events, especially relations between historical medical events and target medical events.