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Detailed phenotype information is fundamental to accurate diagnosis and riskestimation of diseases. As a rich source of phenotype information, electronichealth records (EHRs) promise to empower diagnostic variant interpretation.However, how to accurately and efficiently extract phenotypes from theheterogeneous EHR data remains a challenge. In this work, we present PheME, anEnsemble framework using Multi-modality data of structured EHRs andunstructured clinical notes for accurate Phenotype prediction. Firstly, weemploy multiple deep neural networks to learn reliable representations from thesparse structured EHR data and redundant clinical notes. A multi-modal modelthen aligns multi-modal features onto the same latent space to predictphenotypes. Secondly, we leverage ensemble learning to combine outputs fromsingle-modal models and multi-modal models to improve phenotype predictions. Wechoose seven diseases to evaluate the phenotyping performance of the proposedframework. Experimental results show that using multi-modal data significantlyimproves phenotype prediction in all diseases, the proposed ensemble learningframework can further boost the performance.

PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data