Genetics in MedicineGenomicsPeer-reviewed journal article
ClinPhen extracts and prioritizes patientphenotypes directly from medical records to accelerate genetic disease diagnosis
Cole A. DeisserothiD, Johannes A. BirgmeieriD, Ethan E. BodleiD, Jennefer N. Kohler, Dena R. MataloniD, Yelena Nazarenko, Casie A. GenettiiD, Donna M. BrowniD, Klaus Schmitz‐AbeiD, Kelly Schoch, Heidi CopeiD, Rebecca H. SigneriD, Julián A. Martínez-AgostoiD, Vandana ShashiiD, Alan H. BeggsiD, Matthew Thomas WheeleriD, Jonathan Adam BernsteiniD, Gill BejeranoiD
Abstract
PurposeSevere genetic diseases affect 7 million births per year, worldwide. Diagnosing these diseases is necessary for optimal care, but it can involve the manual evaluation of hundreds of genetic variants per case, with many variants taking an hour to evaluate. Automatic gene-ranking approaches shorten this process by reporting which of the genes containing variants are most likely to be causing the patients symptoms. To use these tools, busy clinicians must manually encode patient phenotypes, which is a cumbersome and imprecise process. With 60 million patients expected to be sequenced in the next 7 years, a fast alternative to manual phenotype extraction from the clinical notes in patients medical records will become necessary.\n\nMethodsWe introduce ClinPhen: a fast, high-accuracy tool that automatically converts the clinical notes into a prioritized list of patient symptoms using HPO terms.\n\nResultsClinPhen shows superior accuracy to existing phenotype extractors, and when paired with a gene-ranking tool it significantly improve the latters performance.\n\nConclusionCompared to manual phenotype extraction, ClinPhen saves more than 5 hours per case in Mendelian diagnosis alone. Summing over millions of forthcoming cases whose medical notes await phenotype encoding, ClinPhen makes a substantial contribution towards ending all patients diagnostic odyssey.
- organism: human
- assay: electronic health records
- research focus: benchmarking
- approach: statistical model
- Citations
- 105
- FWCI
- 3.47
- Field percentile
- top 6%
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