ArticleGenetics in medicine : official journal of the American College of Medical Genetics2019
ClinPhen extracts and prioritizes patient phenotypes directly from medical records to expedite genetic disease diagnosis.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02450851 (Clinical and Genetic Evaluation of Patients With Undiagnosed Disorders Through the Undiagnosed Diseases Network), which is not on this map. Cited by 63 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Clinical and Genetic Evaluation of Patients With Undiagnosed Disorders Through the Undiagnosed Diseases Network
Who cites it
63 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Validation of a Semiautomated Natural Language Processing-Based Procedure for Meta-Analysis of Cancer Susceptibility Gene Penetrance.JCO clinical cancer informatics · 2019Pooled it
- PhenoSS: phenotype semantic similarity-based approach for rare disease prediction and patient clustering.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.Neurology. Clinical practice · 2026Observational
- AutoPCR: automated phenotype concept recognition by prompting.Bioinformatics (Oxford, England) · 2026Article
- A foundational model encodes deep phenotyping data and enables diverse downstream applications.NPJ digital medicine · 2026Article
- PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clustering.medRxiv : the preprint server for health sciences · 2026Article
- Drivers of Diagnostic Delay in Mitochondrial Disease: Missed Recognition of Canonical Features.JIMD reports · 2026Article
- ClinFly: an all-in-one method to translate, de-identify, and summarize medical reports in HPO format.NAR genomics and bioinformatics · 2025Article
- Information content as a health system screening tool for rare diseases.NPJ digital medicine · 2025Article
- Generative Models and Sentence Transformers for the Recognition and Normalization of Continuous and Discontinuous Phenotype Mentions: Model Development and Evaluation.JMIR medical informatics · 2025Article
- An optimized variant prioritization process for rare disease diagnostics: recommendations for Exomiser and Genomiser.Genome medicine · 2025Article
- geneEX: An Integrated Phenotype-Driven Algorithm for Rapid Identification of Causative Variants in Monogenic Disorders.Molecular genetics & genomic medicine · 2025Article
- Improving automated deep phenotyping through large language models using retrieval-augmented generation.Genome medicine · 2025Article
- MPSE identifies newborns for whole genome sequencing within 48 h of NICU admission.NPJ genomic medicine · 2025Article
- PhenoDP: leveraging deep learning for phenotype-based case reporting, disease ranking, and symptom recommendation.Genome medicine · 2025Article
- A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.NPJ digital medicine · 2025Article
- A simplified retriever to improve accuracy of phenotype normalizations by large language models.Frontiers in digital health · 2025Article
- Enhancing human phenotype ontology term extraction through synthetic case reports and embedding-based retrieval: A novel approach for improved biomedical data annotation.Journal of pathology informatics · 2025Article
- PheNormGPT: a framework for extraction and normalization of key medical findings.Database : the journal of biological databases and curation · 2024Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors.
Funding
Abstract
purposeDiagnosing 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.
methodsWe 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.
resultsClinPhen shows superior accuracy and 20× speedup over existing phenotype extractors, and its novel phenotype prioritization scheme improves the performance of gene-ranking tools.
conclusionWhile 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.
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