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SubmittedOpen Access (License Unspecified), Open
Journal article
Enriching representation learning using 53 million patient notes through human phenotype ontology embedding
Artificial intelligence in medicine, v 139, 102523
01 May 2023
PMID: 37100502
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The Human Phenotype Ontology (HPO) is a dictionary of >15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from >1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass current computational models. Furthermore, our embedding technique exhibits a high degree of agreement with domain experts' judgment. By transforming complex and multidimensional phenotypes from the HPO format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction.
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Details
- Title
- Enriching representation learning using 53 million patient notes through human phenotype ontology embedding
- Creators
- Maryam Daniali (Corresponding Author) - Drexel University, Computer Science (Computing)Peter D GalerDavid Lewis-Smith - Children's Hospital of PhiladelphiaShridhar ParthasarathyEdward Kim - Drexel University, Computer Science (Computing)Dario D Salvucci - Drexel University, Computer Science (Computing)Jeffrey M Miller - Children's Hospital of PhiladelphiaScott Haag - Drexel University, Computer Science (Computing)Ingo Helbig - Children's Hospital of Philadelphia
- Publication Details
- Artificial intelligence in medicine, v 139, 102523
- Grant note
- 203914/Z/16/Z / Wellcome Trust K02 NS112600 / NINDS NIH HHS UL1 TR001878 / NCATS NIH HHS U54 HD086984 / NICHD NIH HHS Wellcome Trust U24 NS120854 / NINDS NIH HHS U54 NS108874 / NINDS NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Web of Science ID
- WOS:000965438500001
- Scopus ID
- 2-s2.0-85151438679
- Other Identifier
- 991021861285704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Biomedical
- Medical Informatics