2024
Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data
He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Li Z, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data. Alzheimer's & Dementia Diagnosis Assessment & Disease Monitoring 2024, 16: e12613. PMID: 38966622, PMCID: PMC11220631, DOI: 10.1002/dad2.12613.Peer-Reviewed Original ResearchElectronic health record dataElectronic health recordsComputable phenotypeHealth record dataManual chart reviewHealth recordsAlzheimer's diseaseDiagnosis codesRecord dataChart reviewUTHealthAlzheimer's disease patientsUniversity of MinnesotaAD diagnosisAD identificationDisease patientsPatientsAlzheimerAD patientsDemographicsDiagnosisDiseaseCodeDataUniversityDeveloping deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records
Li Z, Lan L, Zhou Y, Li R, Chavin K, Xu H, Li L, Shih D, Zheng W. Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records. Journal Of Biomedical Informatics 2024, 152: 104626. PMID: 38521180, DOI: 10.1016/j.jbi.2024.104626.Peer-Reviewed Original ResearchDeep learning modelsElectronic health recordsHCC risk predictionHealth recordsTime-varying covariatesLearning modelsElectronic health record dataRisk predictionHealth record dataAccuracy of deep learning modelsDeep learning-based strategyCovariate imbalanceDisease prediction tasksLearning-based strategyDeep learning performanceDisease risk predictionEHR databaseClassification problemLength of follow-upTransfer learningFatty liver diseasePrediction taskCarcinoma riskModel trainingRecord data
2022
The All of Us Research Program: Data quality, utility, and diversity
Ramirez A, Sulieman L, Schlueter D, Halvorson A, Qian J, Ratsimbazafy F, Loperena R, Mayo K, Basford M, Deflaux N, Muthuraman K, Natarajan K, Kho A, Xu H, Wilkins C, Anton-Culver H, Boerwinkle E, Cicek M, Clark C, Cohn E, Ohno-Machado L, Schully S, Ahmedani B, Argos M, Cronin R, O’Donnell C, Fouad M, Goldstein D, Greenland P, Hebbring S, Karlson E, Khatri P, Korf B, Smoller J, Sodeke S, Wilbanks J, Hentges J, Mockrin S, Lunt C, Devaney S, Gebo K, Denny J, Carroll R, Glazer D, Harris P, Hripcsak G, Philippakis A, Roden D, Program T, Ahmedani B, Johnson C, Ahsan H, Antoine-LaVigne D, Singleton G, Anton-Culver H, Topol E, Baca-Motes K, Steinhubl S, Wade J, Begale M, Jain P, Sutherland S, Lewis B, Korf B, Behringer M, Gharavi A, Goldstein D, Hripcsak G, Bier L, Boerwinkle E, Brilliant M, Murali N, Hebbring S, Farrar-Edwards D, Burnside E, Drezner M, Taylor A, Channamsetty V, Montalvo W, Sharma Y, Chinea C, Jenks N, Cicek M, Thibodeau S, Holmes B, Schlueter E, Collier E, Winkler J, Corcoran J, D’Addezio N, Daviglus M, Winn R, Wilkins C, Roden D, Denny J, Doheny K, Nickerson D, Eichler E, Jarvik G, Funk G, Philippakis A, Rehm H, Lennon N, Kathiresan S, Gabriel S, Gibbs R, Rico E, Glazer D, Grand J, Greenland P, Harris P, Shenkman E, Hogan W, Igho-Pemu P, Pollan C, Jorge M, Okun S, Karlson E, Smoller J, Murphy S, Ross M, Kaushal R, Winford E, Wallace F, Khatri P, Kheterpal V, Ojo A, Moreno F, Kron I, Peterson R, Menon U, Lattimore P, Leviner N, Obedin-Maliver J, Lunn M, Malik-Gagnon L, Mangravite L, Marallo A, Marroquin O, Visweswaran S, Reis S, Marshall G, McGovern P, Mignucci D, Moore J, Munoz F, Talavera G, O'Connor G, O'Donnell C, Ohno-Machado L, Orr G, Randal F, Theodorou A, Reiman E, Roxas-Murray M, Stark L, Tepp R, Zhou A, Topper S, Trousdale R, Tsao P, Weidman L, Weiss S, Wellis D, Whittle J, Wilson A, Zuchner S, Zwick M. The All of Us Research Program: Data quality, utility, and diversity. Patterns 2022, 3: 100570. PMID: 36033590, PMCID: PMC9403360, DOI: 10.1016/j.patter.2022.100570.Peer-Reviewed Original Research
2020
Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events
Li F, Du J, He Y, Song H, Madkour M, Rao G, Xiang Y, Luo Y, Chen H, Liu S, Wang L, Liu H, Xu H, Tao C. Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events. Journal Of The American Medical Informatics Association 2020, 27: 1046-1056. PMID: 32626903, PMCID: PMC7647306, DOI: 10.1093/jamia/ocaa058.Peer-Reviewed Original ResearchConceptsTime Event OntologyComplex temporal relationsEvent ontologyNatural language processing fieldTemporal relationsTime-related queriesInformation annotationProcessing fieldTemporal informationData propertiesRelation representationClinical narrativesSemantic representationElectronic health record dataRich setHealth record dataOntologyStrong capabilityReasoningSetQueriesOrder relationRecord dataRepresentationPrimitives
2013
Applying active learning to high-throughput phenotyping algorithms for electronic health records data
Chen Y, Carroll R, Hinz E, Shah A, Eyler A, Denny J, Xu H. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. Journal Of The American Medical Informatics Association 2013, 20: e253-e259. PMID: 23851443, PMCID: PMC3861916, DOI: 10.1136/amiajnl-2013-001945.Peer-Reviewed Original ResearchConceptsActive learningUnrefined featuresSupervised Machine Learning AlgorithmsRefined featuresPhenotyping algorithmElectronic health record dataMachine Learning AlgorithmsHealth record dataVenous thromboembolismRheumatoid arthritisFeature engineeringDomain expertsDomain knowledgePhenotyping tasksLearning algorithmFeature setsLearning approachColorectal cancerAL approachCurve scorePassive learning approachHigh-throughput phenotyping methodsAlgorithmSmall setRecord data
2010
MedEx: a medication information extraction system for clinical narratives
Xu H, Stenner S, Doan S, Johnson K, Waitman L, Denny J. MedEx: a medication information extraction system for clinical narratives. Journal Of The American Medical Informatics Association 2010, 17: 19-24. PMID: 20064797, PMCID: PMC2995636, DOI: 10.1197/jamia.m3378.Peer-Reviewed Original ResearchConceptsClinic visit notesVisit notesMedication informationClinical notesDischarge summariesElectronic medical record dataMedical record dataElectronic medical recordsMedication dataMedical recordsClinical dataClinical researchRecord dataHealthcare safetyDrug namesMedexF-measureClinical narrativesNatural language processing systemsInformation extraction system