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The extraction of phenotype information which is naturally contained inelectronic health records (EHRs) has been found to be useful in variousclinical informatics applications such as disease diagnosis. However, due toimprecise descriptions, lack of gold standards and the demand for efficiency,annotating phenotypic abnormalities on millions of EHR narratives is stillchallenging. In this work, we propose a novel unsupervised deep learningframework to annotate the phenotypic abnormalities from EHRs via semanticlatent representations. The proposed framework takes the advantage of HumanPhenotype Ontology (HPO), which is a knowledge base of phenotypicabnormalities, to standardize the annotation results. Experiments have beenconducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitativeanalysis have shown the proposed framework achieves state-of-the-art annotationperformance and computational efficiency compared with other methods.

Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records