2024
Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction
Huang X, Arora J, Erzurumluoglu A, Stanhope S, Lam D, Arora J, Erzurumluoglu A, Lam D, Khoueiry P, Jensen J, Cai J, Lawless N, Kriegl J, Ding Z, de Jong J, Zhao H, Ding Z, Wang Z, de Jong J. Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction. Journal Of The American Medical Informatics Association 2024, 32: 435-446. PMID: 39723811, PMCID: PMC11833479, DOI: 10.1093/jamia/ocae297.Peer-Reviewed Original ResearchConceptsElectronic health recordsDisease risk predictionElectronic health record researchFamily health historyGenetic aspects of diseaseRisk predictionInflammatory bowel disease subtypeHealth recordsHealth historyAspects of diseaseFamily relationsHealthcare ResearchPatient's disease riskInfluence of geneticsDisease riskDiagnosis dataFamily pedigreeEnvironmental exposuresRisk factorsDisease dependencyPatient representation learningClinical profileFamilyDisease subtypesRisk
2023
Early breast cancer risk detection: a novel framework leveraging polygenic risk scores and machine learning
Tao L, Ye Y, Zhao H. Early breast cancer risk detection: a novel framework leveraging polygenic risk scores and machine learning. Journal Of Medical Genetics 2023, 60: 960-964. PMID: 37055164, DOI: 10.1136/jmg-2022-108582.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceBreast NeoplasmsFemaleGenetic Predisposition to DiseaseHumansMachine LearningRisk FactorsConceptsBreast cancerPolygenic risk scoresRisk scoreBC risk assessmentClinical breast examNon-genetic risk factorsHigh-risk individualsFemale participantsBreast examCancer deathCommon cancerBC screeningRisk factorsBC diagnosisDisease risk predictionDiagnostic stepsPopulation screeningGenetic riskRisk predictionUK BiobankCancerDiagnosisDiagnostic pipelineWomenDetection test
2019
Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies
Fang H, Hui Q, Lynch J, Honerlaw J, Assimes T, Huang J, Vujkovic M, Damrauer S, Pyarajan S, Gaziano J, DuVall S, O’Donnell C, Cho K, Chang K, Wilson P, Tsao P, Sun Y, Tang H, Gaziano J, Ramoni R, Breeling J, Chang K, Huang G, Muralidhar S, O’Donnell C, Tsao P, Muralidhar S, Moser J, Whitbourne S, Brewer J, Concato J, Warren S, Argyres D, Stephens B, Brophy M, Humphries D, Do N, Shayan S, Nguyen X, Pyarajan S, Cho K, Hauser E, Sun Y, Zhao H, Wilson P, McArdle R, Dellitalia L, Harley J, Whittle J, Beckham J, Wells J, Gutierrez S, Gibson G, Kaminsky L, Villareal G, Kinlay S, Xu J, Hamner M, Haddock K, Bhushan S, Iruvanti P, Godschalk M, Ballas Z, Buford M, Mastorides S, Klein J, Ratcliffe N, Florez H, Swann A, Murdoch M, Sriram P, Yeh S, Washburn R, Jhala D, Aguayo S, Cohen D, Sharma S, Callaghan J, Oursler K, Whooley M, Ahuja S, Gutierrez A, Schifman R, Greco J, Rauchman M, Servatius R, Oehlert M, Wallbom A, Fernando R, Morgan T, Stapley T, Sherman S, Anderson G, Sonel E, Boyko E, Meyer L, Gupta S, Fayad J, Hung A, Lichy J, Hurley R, Robey B, Striker R. Harmonizing Genetic Ancestry and Self-identified Race/Ethnicity in Genome-wide Association Studies. American Journal Of Human Genetics 2019, 105: 763-772. PMID: 31564439, PMCID: PMC6817526, DOI: 10.1016/j.ajhg.2019.08.012.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsEthnicityGenome-Wide Association StudyHumansMachine LearningRacial GroupsSupport Vector Machine
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