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References from Lost in transformation: diagnostic aggregation and semantic drift in ICD-derived kidney phenotypes. Local targets link to admitted publications; unresolved targets remain external evidence.
Returning integrated genomic risk and clinical recommendations: the eMERGE study
10.1016/j.gim.2023.100006 · 2023 · External reference
Making work visible for electronic phenotype implementation: lessons learned from the eMERGE network
10.1016/j.jbi.2019.103293 · 2019 · External reference
Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: a case study
2008 · External reference
ICD-10 codes for APOL1-mediated kidney disease
10.1016/j.kint.2025.09.025 · 2026 · External reference
Developing a FHIR-based EHR phenotyping framework: a case study for identification of patients with obesity and multiple comorbidities from discharge summaries
10.1016/j.jbi.2019.103310 · 2019 · External reference
PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability
10.1093/jamia/ocv202 · 2016 · External reference
Characterizing workflow for pediatric asthma patients in emergency departments using electronic health records
10.1016/j.jbi.2015.08.018 · 2015 · External reference
Severity prediction for COVID-19 patients via recurrent neural networks
2021 · External reference
High prevalence of unlabeled chronic kidney disease among inpatients at a tertiary-care hospital
10.1097/maj.0b013e318181288e · 2009 · External reference
Measuring diagnoses: ICD code accuracy
10.1111/j.1475-6773.2005.00444.x · 2005 · External reference
Copy number variant analysis and genome-wide association study identify loci with large effect for vesicoureteral reflux
10.1681/asn.2020050681 · 2021 · External reference
Phenotype risk scores identify patients with unrecognized Mendelian disease patterns
10.1126/science.aal4043 · 2018 · External reference
Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology
10.1186/1471-2105-15-248 · 2014 · External reference
HPO2Vec+: leveraging heterogeneous knowledge resources to enrich node embeddings for the human phenotype ontology
10.1016/j.jbi.2019.103246 · 2019 · External reference
Online Mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders
10.1093/nar/gki033 · 2005 · External reference
The human phenotype ontology: a tool for annotating and analyzing human hereditary disease
10.1016/j.ajhg.2008.09.017 · 2008 · External reference
OMIM.org: leveraging knowledge across phenotype-gene relationships
10.1093/nar/gky1151 · 2019 · External reference
Deep phenotyping on electronic health records facilitates genetic diagnosis by clinical exomes
10.1016/j.ajhg.2018.05.010 · 2018 · External reference
Linking rare and common disease vocabularies by mapping between the human phenotype ontology and phecodes
10.1093/jamiaopen/ooad007 · 2023 · External reference
Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records
10.1016/j.gim.2023.100921 · 2023 · External reference
Mondo: integrating disease terminology across communities
10.1093/genetics/iyaf215 · 2026 · External reference
Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies
10.1038/s41746-021-00488-3 · 2021 · External reference
Polygenic risk alters the penetrance of monogenic kidney disease
10.1038/s41467-023-43878-9 · 2023 · External reference
Medical records-based chronic kidney disease phenotype for clinical care and ‘big data’ observational and genetic studies
10.1038/s41746-021-00428-1 · 2021 · External reference
Genome-wide association analyses define pathogenic signaling pathways and prioritize drug targets for IgA nephropathy
10.1038/s41588-023-01422-x · 2023 · External reference
The eMERGE genotype set of 83,717 subjects imputed to ∼40 million variants genome wide and association with the herpes zoster medical record phenotype
2019 · External reference
Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits
10.1038/s41467-022-34456-6 · 2022 · External reference
Deep phenotyping: embracing complexity and temporality-towards scalability, portability, and interoperability
10.1016/j.jbi.2020.103433 · 2020 · External reference
OHDSI standardized vocabularies - a large-scale centralized reference ontology for international data harmonization
10.1093/jamia/ocad247 · 2024 · External reference
Effect of vocabulary mapping for conditions on phenotype cohorts
10.1093/jamia/ocy124 · 2018 · External reference
Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO
10.1186/s12918-019-0697-8 · 2019 · External reference
Adapting electronic health records-derived phenotypes to claims data: lessons learned in using limited clinical data for phenotyping
10.1016/j.jbi.2019.103363 · 2020 · External reference
Coverage of rare disease names in standard terminologies and implications for patients, providers, and research
2014 · External reference
Characterizing database granularity using SNOMED-CT hierarchy
2020 · External reference
Mapping of UK Biobank clinical codes: challenges and possible solutions
10.1371/journal.pone.0275816 · 2022 · External reference
The Human Phenotype Ontology in 2024: phenotypes around the world
10.1093/nar/gkad1005 · 2024 · External reference
PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations
10.1093/bioinformatics/btq126 · 2010 · External reference
Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data
10.1038/nbt.2749 · 2013 · External reference
Phenome-wide association analysis suggests the APOL1 linked disease spectrum primarily drives kidney-specific pathways
10.1016/j.kint.2020.01.027 · 2020 · External reference
Genome-wide polygenic score to predict chronic kidney disease across ancestries
10.1038/s41591-022-01869-1 · 2022 · External reference
GWAS and enrichment analyses of non-alcoholic fatty liver disease identify new trait-associated genes and pathways across eMERGE Network
10.1186/s12916-019-1364-z · 2019 · External reference
Generalizability of polygenic risk scores for breast cancer among women with European, African, and Latinx ancestry
10.1001/jamanetworkopen.2021.19084 · 2021 · External reference
Enhanced rare disease mapping for phenome-wide genetic association in the UK Biobank
10.1186/s13073-022-01094-y · 2022 · External reference
Urobiota analysis and genome-wide association study in pediatric recurrent urinary tract infections and vesicoureteral reflux
10.1172/jci.insight.199689 · 2026 · External reference
Phenome-wide association study identifies multiple traits associated with a polygenic risk score for colorectal cancer
10.1186/s40246-025-00791-0 · 2025 · External reference
PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies
10.1093/jamia/ocy056 · 2018 · External reference
Developing and evaluating pediatric phecodes (peds-phecodes) for high-throughput phenotyping using electronic health records
10.1093/jamia/ocad233 · 2024 · External reference
Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record
10.1371/journal.pone.0175508 · 2017 · External reference
Facilitating phenotype transfer using a common data model
10.1016/j.jbi.2019.103253 · 2019 · External reference
Unresolved reference
2025 · External reference
Observational health data sciences and informatics (OHDSI): opportunities for observational researchers
2015 · External reference
Implementing a common data model in ophthalmology: mapping structured electronic health record ophthalmic examination data to standard vocabularies
10.1016/j.xops.2024.100666 · 2025 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2025 · External reference
The unified medical language system: an informatics research collaboration
10.1136/jamia.1998.0050001 · 1998 · External reference
The UMLS knowledge sources at 30: indispensable to current research and applications in biomedical informatics
10.1093/jamia/ocaa208 · 2020 · External reference
The unified medical language system (UMLS): integrating biomedical terminology
10.1093/nar/gkh061 · 2004 · External reference
Transforming and evaluating the UK Biobank to the OMOP common data model for COVID-19 research and beyond
10.1093/jamia/ocac203 · 2022 · External reference
The sequences of 150,119 genomes in the UK Biobank
10.1038/s41586-022-04965-x · 2022 · External reference
Return of individual research results from genome-wide association studies: experience of the electronic medical records and genomics (eMERGE) network
10.1038/gim.2012.15 · 2012 · External reference
Electronic medical records for genetic research: results of the eMERGE consortium
10.1126/scitranslmed.3001807 · 2011 · External reference
10.1016/j.ajhg.2019.07.018
10.1016/j.ajhg.2019.07.018 · 2019 · External reference
Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example
10.1093/jamia/ocy051 · 2018 · External reference
Heritability and genome-wide association study of benign prostatic hyperplasia (BPH) in the eMERGE network
10.1038/s41598-019-42427-z · 2019 · External reference
The ‘All of Us’ Research Program
10.1056/nejmsr1809937 · 2019 · External reference
The All of Us Data and Research Center: creating a secure, scalable, and sustainable ecosystem for biomedical research
10.1146/annurev-biodatasci-122120-104825 · 2023 · External reference
The All of Us Research Program: data quality, utility, and diversity
10.1016/j.patter.2022.100570 · 2022 · External reference
Unresolved reference
2025 · External reference
Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics
10.1093/bioinformatics/btad655 · 2023 · External reference
Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease
10.1093/jamia/ocz179 · 2019 · External reference
Implications of mappings between International Classification of Diseases clinical diagnosis codes and human phenotype ontology terms
10.1093/jamiaopen/ooae118 · 2024 · External reference
Can billing codes accurately identify rapidly progressing stage 3 and stage 4 chronic kidney disease patients: a diagnostic test study
10.1186/s12882-019-1429-4 · 2019 · External reference
The ICD-9 to ICD-10 transition has not improved identification of rapidly progressing stage 3 and stage 4 chronic kidney disease patients: a diagnostic test study
10.1186/s12882-024-03478-1 · 2024 · External reference
A retrospective multi-site examination of chronic kidney disease using longitudinal laboratory results and metadata to identify clinical and financial risk
10.1186/s12882-024-03869-4 · 2024 · External reference
Assessing the accuracy of ICD-10 coding for measuring rates of and mortality from acute kidney injury and the impact of electronic alerts: an observational cohort study
10.1093/ckj/sfz117 · 2020 · External reference
Accuracy of identification of Cardiovascular Events with International Classification of Diseases Diagnosis Codes versus physician adjudication in CKD and kidney failure
10.1681/asn.0000000874 · 2026 · External reference
Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records
10.1093/jamia/ocaf055 · 2025 · External reference
Mapping ICD-10 and ICD-10-CM codes to phecodes: workflow development and initial evaluation
10.2196/14325 · 2019 · External reference
Using phecodes for research with the electronic health record: from PheWAS to PheRS
10.1146/annurev-biodatasci-122320-112352 · 2021 · External reference
Proposed framework for presenting injury data using The International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes
2016 · External reference
Accuracy and completeness of clinical coding using ICD-10 for ambulatory visits
2017 · External reference