  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. In this paper, we propose a novel neural network based
on attention mechanism, called cross-event attention-based time-aware network
(CATNet), for medical event prediction. It is a time-aware, event-aware and
task-adaptive method with the following advantages: 1) modeling heterogeneous
information and temporal information in a unified way and considering temporal
irregular characteristics locally and globally respectively, 2) taking full
advantage of correlations among different types of events via cross-event
attention. Experiments on two public datasets (MIMIC-III and eICU) show CATNet
can be adaptive with different MEP tasks and outperforms other state-of-the-art
methods on various MEP tasks. The source code of CATNet will be released after
this manuscript is accepted.
