2 research outputs found
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
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.Comment: Accepted by BIBM 2019 (Regular