2023/03/19 by Shenghan Zhang, Haoxuan Li, Zhang, Shenghan +13
Biochemistry, Genetics and Molecular Biology · Computer Science · Health Professions · #Artificial Intelligence in Healthcare #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Multimedia (cs.MM) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2303.10794
openalex publication_date 2023/03/19 · openalex created_date 2023/03/23 · openalex updated_date 2026/07/28
Detailed phenotype information is fundamental to accurate diagnosis and risk estimation of diseases. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from the heterogeneous EHR data remains a challenge. In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction. Firstly, we employ multiple deep neural networks to learn reliable representations from the sparse structured EHR data and redundant clinical notes. A multi-modal model then aligns multi-modal features onto the same latent space to predict phenotypes. Secondly, we leverage ensemble learning to combine outputs from single-modal models and multi-modal models to improve phenotype predictions. We choose seven diseases to evaluate the phenotyping performance of the proposed framework. Experimental results show that using multi-modal data significantly improves phenotype prediction in all diseases, the proposed ensemble learning framework can further boost the performance.