Medical Concept Representation Learning from Electronic Health Records and its Application on Heart Failure Prediction

TL;DR

This paper introduces a novel data-driven method to learn meaningful medical concept representations from electronic health records, leveraging temporal co-occurrence patterns, which enhances heart failure prediction accuracy.

Contribution

The study presents a new approach to represent medical concepts based on their temporal co-occurrence in EHR data, improving predictive modeling for heart failure.

Findings

Significantly improves heart failure prediction performance (up to 23% AUC increase).

Effectively maps related medical concepts together based on co-occurrence.

Enhances predictive accuracy across multiple classification methods.

Abstract

Objective: To transform heterogeneous clinical data from electronic health records into clinically meaningful constructed features using data driven method that rely, in part, on temporal relations among data. Materials and Methods: The clinically meaningful representations of medical concepts and patients are the key for health analytic applications. Most of existing approaches directly construct features mapped to raw data (e.g., ICD or CPT codes), or utilize some ontology mapping such as SNOMED codes. However, none of the existing approaches leverage EHR data directly for learning such concept representation. We propose a new way to represent heterogeneous medical concepts (e.g., diagnoses, medications and procedures) based on co-occurrence patterns in longitudinal electronic health records. The intuition behind the method is to map medical concepts that are co-occuring closely in time to similar concept vectors so that their distance will be small. We also derive a simple method to construct patient vectors from the related medical concept vectors. Results: For qualitative evaluation, we study similar medical concepts across diagnosis, medication and procedure. In quantitative evaluation, our proposed representation significantly improves the predictive modeling performance for onset of heart failure (HF), where classification methods (e.g. logistic regression, neural network, support vector machine and K-nearest neighbors) achieve up to 23% improvement in area under the ROC curve (AUC) using this proposed representation. Conclusion: We proposed an effective method for patient and medical concept representation learning. The resulting representation can map relevant concepts together and also improves predictive modeling performance.