PatSimBoosting: Enhancing Patient Representations for Disease Prediction Through Similarity Analysis

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 Learning Module (PRLM), and Prediction Module (PM). FEM extracts trends and variations of dynamic features, while SCM employs Dynamic Time Warping (DTW) to assess the similarity between patients. PRLM learns patient representations, and the PM utilizes the representation of the most similar patient, along with the current patient’s representation, to perform disease prediction. Experimental results on two real-world public datasets demonstrate that PatSimBoost outperforms existing state-of-the-art methods in terms of F1-score, AUROC, and AUPRC.