ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis.

Published

Journal Article

PURPOSE: Diagnosing monogenic diseases facilitates optimal care, but can involve the manual evaluation of hundreds of genetic variants per case. Computational tools like Phrank expedite this process by ranking all candidate genes by their ability to explain the patient's phenotypes. To use these tools, busy clinicians must manually encode patient phenotypes from lengthy clinical notes. With 100 million human genomes estimated to be sequenced by 2025, a fast alternative to manual phenotype extraction from clinical notes will become necessary. METHODS: We introduce ClinPhen, a fast, high-accuracy tool that automatically converts clinical notes into a prioritized list of patient phenotypes using Human Phenotype Ontology (HPO) terms. RESULTS: ClinPhen shows superior accuracy and 20× speedup over existing phenotype extractors, and its novel phenotype prioritization scheme improves the performance of gene-ranking tools. CONCLUSION: While a dedicated clinician can process 200 patient records in a 40-hour workweek, ClinPhen does the same in 10 minutes. Compared with manual phenotype extraction, ClinPhen saves an additional 3-5 hours per Mendelian disease diagnosis. Providers can now add ClinPhen's output to each summary note attached to a filled testing laboratory request form. ClinPhen makes a substantial contribution to improvements in efficiency critically needed to meet the surging demand for clinical diagnostic sequencing.

Full Text

Duke Authors

Cited Authors

  • Deisseroth, CA; Birgmeier, J; Bodle, EE; Kohler, JN; Matalon, DR; Nazarenko, Y; Genetti, CA; Brownstein, CA; Schmitz-Abe, K; Schoch, K; Cope, H; Signer, R; Undiagnosed Diseases Network, ; Martinez-Agosto, JA; Shashi, V; Beggs, AH; Wheeler, MT; Bernstein, JA; Bejerano, G

Published Date

  • July 2019

Published In

Volume / Issue

  • 21 / 7

Start / End Page

  • 1585 - 1593

PubMed ID

  • 30514889

Pubmed Central ID

  • 30514889

Electronic International Standard Serial Number (EISSN)

  • 1530-0366

Digital Object Identifier (DOI)

  • 10.1038/s41436-018-0381-1

Language

  • eng

Conference Location

  • United States