Bi-dimensional Representation of Patients for Diagnosis Prediction

Previous work on learning representation for patients from Electronic Health Records (EHRs) has succeeded in assisting medical diagnosis. Three perspectives of records, patient symptoms, medical treatments and diagnosis codes, are consisted in EHRs. However, existing approaches on patient representation learning take one perspective of patient symptoms and medical treatments into consideration, which miss out the latent correlations between them. Actually, based on the sequence of hospital visits, physical symptoms and associated treatments together affect the diagnosis and recovery of patients. In this paper, we propose Patient2vec, a novel model to learn the hi-dimensional representation for patients by jointly extracting features from physical symptoms and medical treatments. We introduce RNN model into Patient2vec to learn the sequential context-aware features of visits. The learned representations are then fed into a classifier to diagnosis prediction. Experiments on public dataset through multi-classification tasks indicate that Patient2vec achieves up to 76% improvement in area under the ROC curve (AUC) on average, demonstrating that our method significantly outperforms single dimension representation for patients.