PatSimBoosting: Enhancing Patient Representations for Disease Prediction Through Similarity Analysis

Yuzheng Yan, Ziyue Yu, Wuman Luo

Abstract: Patient representation learning based on electronic health records (EHR) is crucial for disease prediction. So far, various deep learning-based methods have been proposed and have made great progress. In particular, recent research has shown that trends and variations of dynamic features are of great importance in patient representation learning. However, these methods ignored the similarity between the patients. Although a number of similarity-based methods have been proposed for patient representation learning, they regarded each dynamic feature as a whole in similarity detection and failed to utilize the important fine-grained characteristics of each feature. To address this issue, we propose a Patient Similarity-Based Representation Boosting framework (PatSimBoost) to enhance patient representation for disease prediction based on EHR. Our proposed framework consists of four modules: Frequency Extraction Module (FEM), Similarity Calculation Module (SCM), Patient Representation Lea

External IDs:dblp:conf/iotbd/YanYL25