ArticleJournal of the American Medical Informatics Association : JAMIA2019
Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Interpretable fine-tuned large language models facilitate making genetic test decisions for rare diseases.NPJ digital medicine · 2026Article
- Developing a phenotype risk score formedRxiv : the preprint server for health sciences · 2026Article
- Residual allelic activity likely underlies the low rates of disease expression for predicted loss-of-function variants in population-scale biobanks.American journal of human genetics · 2025Article
- Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine.Biomedicines · 2025Review
- Systematic method for classifying multiple congenital anomaly cases in electronic health records.Genetics in medicine : official journal of the American College of Medical Genetics · 2025Article
- Characterizing trends in clinical genetic testing: A single-center analysis of EHR data from 1.8 million patients over two decades.American journal of human genetics · 2025Article
- A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit.NPJ digital medicine · 2025Article
- Developing a phenotype risk score for tic disorders in a large, clinical biobank.Translational psychiatry · 2024Article
- A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit.medRxiv : the preprint server for health sciences · 2024Article
- Identifying clusters of patient comorbidities associated with obstructive sleep apnea using electronic health records.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024Article
- The Human Phenotype Ontology in 2024: phenotypes around the world.Nucleic acids research · 2024Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- A test of automated use of electronic health records to aid in diagnosis of genetic disease.Genetics in medicine : official journal of the American College of Medical Genetics · 2023Article
- Phenotype-Driven Molecular Genetic Test Recommendation for Diagnosing Pediatric Rare Disorders.Research square · 2023Article
- Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records.Genetics in medicine : official journal of the American College of Medical Genetics · 2023Article
- Ontologizing health systems data at scale: making translational discovery a reality.NPJ digital medicine · 2023Article
- Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes.JAMIA open · 2023Article
- Article
- The phers R package: using phenotype risk scores based on electronic health records to study Mendelian disease and rare genetic variants.Bioinformatics (Oxford, England) · 2022Article
- Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data.Complex psychiatry · 2022Article
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Authors and funding
10 authors.
Funding
Abstract
objectiveThe Phenotype Risk Score (PheRS) is a method to detect Mendelian disease patterns using phenotypes from the electronic health record (EHR). We compared the performance of different approaches mapping EHR phenotypes to Mendelian disease features. MATERIALS AND
methodsPheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms. In previous work, we presented a map linking phecodes (based on International Classification of Diseases [ICD]-Ninth Revision) to HPO terms. For this study, we integrated ICD-Tenth Revision codes and lab data. We also created a new map between HPO terms using customized groupings of ICD codes. We compared the performance with cases and controls for 16 Mendelian diseases using 2.5 million de-identified medical records.
resultsPheRS effectively distinguished cases from controls for all 15 positive controls and all approaches tested (P < 4 × 1016). Adding lab data led to a statistically significant improvement for 4 of 14 diseases. The custom ICD groupings improved specificity, leading to an average 8% increase for precision at 100 (-2% to 22%). Eight of 10 adults with cystic fibrosis tested had PheRS in the 95th percentile prio to diagnosis. DISCUSSION: Both phecodes and custom ICD groupings were able to detect differences between affected cases and controls at the population level. The ICD map showed better precision for the highest scoring individuals. Adding lab data improved performance at detecting population-level differences.
conclusionsPheRS is a scalable method to study Mendelian disease at the population level using electronic health record data and can potentially be used to find patients with undiagnosed Mendelian disease.
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