Enhancing Model Interpretability and Accuracy for Disease Progression Prediction via Phenotype-Based Patient Similarity Learning
This addresses the need for more interpretable and accurate clinical predictive models for healthcare providers, though it appears incremental in its approach.
The paper tackled the problem of predicting disease progression by incorporating patient similarity features learned from medical service patterns, showing that these phenotype-based features improved prediction accuracy for Chronic Lymphocytic Leukemia diagnoses over multiple baseline models.
Models have been proposed to extract temporal patterns from longitudinal electronic health records (EHR) for clinical predictive models. However, the common relations among patients (e.g., receiving the same medical treatments) were rarely considered. In this paper, we propose to learn patient similarity features as phenotypes from the aggregated patient-medical service matrix using non-negative matrix factorization. On real-world medical claim data, we show that the learned phenotypes are coherent within each group, and also explanatory and indicative of targeted diseases. We conducted experiments to predict the diagnoses for Chronic Lymphocytic Leukemia (CLL) patients. Results show that the phenotype-based similarity features can improve prediction over multiple baselines, including logistic regression, random forest, convolutional neural network, and more.