  Electronic health records (EHRs) contain patients' heterogeneous data that
are collected from medical providers involved in the patient's care, including
medical notes, clinical events, laboratory test results, symptoms, and
diagnoses. In the field of modern healthcare, predicting whether patients would
experience any risks based on their EHRs has emerged as a promising research
area, in which artificial intelligence (AI) plays a key role. To make AI models
practically applicable, it is required that the prediction results should be
both accurate and interpretable. To achieve this goal, this paper proposed a
label-dependent and event-guided risk prediction model (LERP) to predict the
presence of multiple disease risks by mainly extracting information from
unstructured medical notes. Our model is featured in the following aspects.
First, we adopt a label-dependent mechanism that gives greater attention to
words from medical notes that are semantically similar to the names of risk
labels. Secondly, as the clinical events (e.g., treatments and drugs) can also
indicate the health status of patients, our model utilizes the information from
events and uses them to generate an event-guided representation of medical
notes. Thirdly, both label-dependent and event-guided representations are
integrated to make a robust prediction, in which the interpretability is
enabled by the attention weights over words from medical notes. To demonstrate
the applicability of the proposed method, we apply it to the MIMIC-III dataset,
which contains real-world EHRs collected from hospitals. Our method is
evaluated in both quantitative and qualitative ways.
