ArticleAmerican journal of human genetics2018
Deep Phenotyping on Electronic Health Records Facilitates Genetic Diagnosis by Clinical Exomes.
Article in American journal of human genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
85 citing papers in PubMed, 1 synthesis or guideline pooled it, 145 citations in OpenAlex.
- Genetic pleiotropy of ERCC6 loss-of-function and deleterious missense variants links retinal dystrophy, arrhythmia, and immunodeficiency in diverse ancestries.Human mutation · 2021Pooled it
- Beyond Pain Intensity: A Patient-Informed Framework for Documenting Burden in Small Fiber Neuropathy.Neurology and therapy · 2026Review
- PhenoSS: phenotype semantic similarity-based approach for rare disease prediction and patient clustering.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Brachydactyly Type A1 Caused by anReports (MDPI) · 2026Article
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 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
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Unifying the odyssey: artificial intelligence for rare disease diagnosis and therapy.Health and technology · 2026Article
- Addressing the diagnostic gap through deep phenotyping.Human genomics · 2026Article
- Assessing genetic counseling efficiency with natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Revealing the Future of Pharmacovigilance in Precision Pharmaceutical Monitoring.Current drug safety · 2026Review
- TimeX: Phenotype Onset Extraction from Clinical Narratives.npj health systems · 2026Article
- 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
- Bibliometric Analysis: Insights Into the Podiatric Medicine Landscape of Diabetic Sensory Peripheral Neuropathy and Genomics.Journal of foot and ankle research · 2025Article
- Improving automated deep phenotyping through large language models using retrieval-augmented generation.Genome medicine · 2025Article
- First steps toward building natural history of diseases computationally: Lessons learned from the Noonan syndrome use case.American journal of human genetics · 2025Article
- A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.NPJ digital medicine · 2025Article
- Integrating deep phenotyping with genetic analysis: a comprehensive workflow for diagnosis and management of rare bone diseases.Orphanet journal of rare diseases · 2024Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
20 authors at 4 institutions in 1 country.
Funding
Abstract
Integration of detailed phenotype information with genetic data is well established to facilitate accurate diagnosis of hereditary disorders. As a rich source of phenotype information, electronic health records (EHRs) promise to empower diagnostic variant interpretation. However, how to accurately and efficiently extract phenotypes from heterogeneous EHR narratives remains a challenge. Here, we present EHR-Phenolyzer, a high-throughput EHR framework for extracting and analyzing phenotypes. EHR-Phenolyzer extracts and normalizes Human Phenotype Ontology (HPO) concepts from EHR narratives and then prioritizes genes with causal variants on the basis of the HPO-coded phenotype manifestations. We assessed EHR-Phenolyzer on 28 pediatric individuals with confirmed diagnoses of monogenic diseases and found that the genes with causal variants were ranked among the top 100 genes selected by EHR-Phenolyzer for 16/28 individuals (p < 2.2 × 10
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.