Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
This work addresses the problem of efficient and accurate phenotype annotation for clinical informatics applications, though it appears incremental as it builds on existing methods with a novel framework.
The paper tackles the challenge of annotating phenotypic abnormalities from electronic health records (EHRs) by proposing an unsupervised deep learning framework that uses semantic latent representations and the Human Phenotype Ontology, achieving state-of-the-art performance and computational efficiency on 52,722 EHRs from the MIMIC-III dataset.
The extraction of phenotype information which is naturally contained in electronic health records (EHRs) has been found to be useful in various clinical informatics applications such as disease diagnosis. However, due to imprecise descriptions, lack of gold standards and the demand for efficiency, annotating phenotypic abnormalities on millions of EHR narratives is still challenging. In this work, we propose a novel unsupervised deep learning framework to annotate the phenotypic abnormalities from EHRs via semantic latent representations. The proposed framework takes the advantage of Human Phenotype Ontology (HPO), which is a knowledge base of phenotypic abnormalities, to standardize the annotation results. Experiments have been conducted on 52,722 EHRs from MIMIC-III dataset. Quantitative and qualitative analysis have shown the proposed framework achieves state-of-the-art annotation performance and computational efficiency compared with other methods.