Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks

Abstract

Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose {\sf Dipole}, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation.

Cited by in corpus (10)

• Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review
• Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction
• ARM-Net: Adaptive Relation Modeling Network for Structured Data
• Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
• Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks
• Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records
• Modeling Long-term Dependencies and Short-term Correlations in Patient Journey Data with Temporal Attention Networks for Health Prediction
• Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data
• PopNet: Real-Time Population-Level Disease Prediction with Data Latency
• Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription