A phenotyping algorithm for classification of single ventricle physiology using electronic health records
Our automated phenotype algorithm, combined with physician adjudication, outperforms a published method for SVP classification. It effectively identifies false positives by cross-referencing clinical notes and detects missed SVP cases that were due to absent or erroneous ICD codes. Our integrated phenotyping algorithm showed excellent performance and has the potential to improve research and clinical care of SVP patients through the automated development of an electronic cohort for prognostication, monitoring, and management.
Authors
- Pierangelo Renella (ORCID: https://orcid.org/0000-0002-6444-2604)
- J. Paul Finn (ORCID: https://orcid.org/0000-0002-4534-7572)
- William W.Y. Hsu (ORCID: https://orcid.org/0000-0002-5168-070X)
- Hang Xu (ORCID: https://orcid.org/0000-0001-6824-6128)
- Ziad Hindosh
- Ramin Badiyan
- Kim‐Lien Nguyen (ORCID: https://orcid.org/0000-0002-8854-2976)
- Gary Satou (ORCID: https://orcid.org/0000-0001-6595-1286)
- Bing Zhu (ORCID: https://orcid.org/0000-0002-5700-1253)
- Majid Husain (ORCID: https://orcid.org/0009-0009-5959-9189)
- Francisco Elisarraras
Institutions
- Children's Hospital of Orange County (US)
- University of California, Los Angeles (US)
- University of California, Irvine (US)
- Mattel Children's Hospital (US)
- VA Greater Los Angeles Healthcare System (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf035
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health