Hospital Readmission Prediction Using Semantic Relations Between Medical Codes

Unexpected hospital readmissions are problematic to both hospitals and patients. Prediction of patients’ readmission becomes an important task. Recurrent neural networks (RNN) and the attention mechanisms have been proposed to learn temporal relationships between patient’ admissions for readmission prediction. Existing works demonstrate that incorporating medical ontologies can be beneficial to prediction tasks. However, it ignores the importance of semantic information of medical codes which can be found in the codes’ descriptions. Therefore, we propose a model called Code Description Attention Model (CDAM), which adopts codes’ descriptions into readmission prediction model via RNN and the attention mechanisms to explore the semantic information about medical codes. Experimental results show that CDAM improves not only the performance of readmission prediction but also the quality of codes’ embeddings.