2023
Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions
Kuo T, Pham A, Edelson M, Kim J, Chan J, Gupta Y, Ohno-Machado L, Anderson D, Balacha C, Bath T, Baxter S, Becker-Pennrich A, Bell D, Bernstam E, Ngan C, Day M, Doctor J, DuVall S, El-Kareh R, Florian R, Follett R, Geisler B, Ghigi A, Gottlieb A, Hinske L, Hu Z, Ir D, Jiang X, Kim K, Kim J, Knight T, Koola J, Kuo T, Lee N, Mansmann U, Matheny M, Meeker D, Mou Z, Neumann L, Nguyen N, Nick A, Ohno-Machado L, Park E, Paul P, Pletcher M, Post K, Rieder C, Scherer C, Schilling L, Soares A, SooHoo S, Soysal E, Steven C, Tep B, Toy B, Wang B, Wu Z, Xu H, Yong C, Zheng K, Zhou Y, Zucker R. Blockchain-enabled immutable, distributed, and highly available clinical research activity logging system for federated COVID-19 data analysis from multiple institutions. Journal Of The American Medical Informatics Association 2023, 30: 1167-1178. PMID: 36916740, PMCID: PMC10198529, DOI: 10.1093/jamia/ocad049.Peer-Reviewed Original ResearchConceptsFederated data analysisUser activity logsSmart contract deploymentRun-time efficiencyData analysis systemData analysis activitiesActivity logsData discoveryQuerying timeBlockchain systemBlockchain technologyNetwork transactionsCOVID-19 data analysisMultiple institutionsLow deploymentBlockchainGitHub repositoryMultiple nodesLarge networksQueriesAnalysis activitiesHigh availabilityLanguage codeBaseline solutionData analysis
2022
Factors Associated With COVID-19 Death in the United States: Cohort Study
Chen U, Xu H, Krause T, Greenberg R, Dong X, Jiang X. Factors Associated With COVID-19 Death in the United States: Cohort Study. JMIR Public Health And Surveillance 2022, 8: e29343. PMID: 35377319, PMCID: PMC9132142, DOI: 10.2196/29343.Peer-Reviewed Original ResearchConceptsCOVID-19-related deathsCohort studyPatient characteristicsElectronic health record data setsLarge national cohort studyNational cohort studyChronic respiratory diseasesProportional hazards modelCOVID-19COVID-19 deathsImmunosuppressive conditionsKidney functionMale sexLung cancerCOVID-19 casesRecent diagnosisRisk factorsRespiratory diseaseCardiac diseaseOrgan transplantsHigh incidenceHazards modelNeurological diseasesOlder ageDiseaseRecurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data
Rasmy L, Nigo M, Kannadath B, Xie Z, Mao B, Patel K, Zhou Y, Zhang W, Ross A, Xu H, Zhi D. Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. The Lancet Digital Health 2022, 4: e415-e425. PMID: 35466079, PMCID: PMC9023005, DOI: 10.1016/s2589-7500(22)00049-8.Peer-Reviewed Original ResearchConceptsLight Gradient Boost MachineFeature engineeringGradient-boosting machineMultiple machine learning modelsElectronic health record dataNeural network-based modelReal-world datasetsRecurrent neural network modelComplex feature engineeringMachine learning modelsBinary classification taskSpecific feature selectionLogistic regression algorithmNeural network modelHealth record dataRecurrent neural network-based modelBinary classification modelNetwork-based modelTraditional machineExtensive data preprocessingHigh prediction accuracyMultiple external datasetsClassification taskData preprocessingFeature selectionCombining human and machine intelligence for clinical trial eligibility querying
Fang Y, Idnay B, Sun Y, Liu H, Chen Z, Marder K, Xu H, Schnall R, Weng C. Combining human and machine intelligence for clinical trial eligibility querying. Journal Of The American Medical Informatics Association 2022, 29: 1161-1171. PMID: 35426943, PMCID: PMC9196697, DOI: 10.1093/jamia/ocac051.Peer-Reviewed Original ResearchConceptsNegation scope detectionCohort queriesScope detectionHealth Information Technology Usability Evaluation ScaleHuman-computer collaborationValue normalizationNatural language processingMachine intelligenceDomain expertsEligibility criteria textUsability evaluationLearnability scoreF1 scoreUser interventionLanguage processingHuman intelligenceUsability scoreQueriesError correctionEngagement featuresIntelligenceDisease trialsFrequent modificationsEnhanced modulesCOVID-19 clinical trialsDLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models
Luo C, Islam M, Sheils N, Buresh J, Reps J, Schuemie M, Ryan P, Edmondson M, Duan R, Tong J, Marks-Anglin A, Bian J, Chen Z, Duarte-Salles T, Fernández-Bertolín S, Falconer T, Kim C, Park R, Pfohl S, Shah N, Williams A, Xu H, Zhou Y, Lautenbach E, Doshi J, Werner R, Asch D, Chen Y. DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models. Nature Communications 2022, 13: 1678. PMID: 35354802, PMCID: PMC8967932, DOI: 10.1038/s41467-022-29160-4.Peer-Reviewed Original ResearchNovel informatics approaches to COVID-19 Research: From methods to applications
Xu H, Buckeridge D, Wang F, Tarczy-Hornoch P. Novel informatics approaches to COVID-19 Research: From methods to applications. Journal Of Biomedical Informatics 2022, 129: 104028. PMID: 35181495, PMCID: PMC8847074, DOI: 10.1016/j.jbi.2022.104028.Peer-Reviewed Original Research
2021
Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database
Dong X, Zhou Y, Shu X, Bernstam E, Stern R, Aronoff D, Xu H, Lipworth L. Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database. Microbiology Spectrum 2021, 9: 10.1128/spectrum.00327-21. PMID: 34406805, PMCID: PMC8552669, DOI: 10.1128/spectrum.00327-21.Peer-Reviewed Original ResearchConceptsPositive SARS-CoV-2 testSARS-CoV-2 testSecond positive testElectronic health record databaseCases of reinfectionHealth record databasePositive testPositive SARS-CoV-2 PCR test resultsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testingSARS-CoV-2 PCR test resultsRecord databaseSevere acute respiratory syndrome coronavirus 2Intensive care unit admissionAcute respiratory syndrome coronavirus 2SARS-CoV-2 infectionRespiratory syndrome coronavirus 2Long-term health consequencesLarge electronic health record databasePotential long-term health consequencesCare unit admissionOverweight/obeseChronic medical conditionsPositive molecular testCOVID-19 patientsSyndrome coronavirus 2The application of artificial intelligence and data integration in COVID-19 studies: a scoping review
Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. Journal Of The American Medical Informatics Association 2021, 28: 2050-2067. PMID: 34151987, PMCID: PMC8344463, DOI: 10.1093/jamia/ocab098.Peer-Reviewed Original ResearchConceptsAI applicationsArtificial intelligenceData integrationHeterogeneous dataSocial media data analysisMost AI applicationsHeterogeneous data sourcesMedia data analysisProteomics data analysisAI algorithmsAI frameworkElectronic health recordsHeterogenous dataBiased algorithmsHealth recordsCOVID-19 researchData analysisSingle-source approachResearch topicData sourcesResearch areaIntelligenceSurveillance systemDifferent sourcesAlgorithmLeveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality
Tortolero G, Brown M, Sharma S, de Oliveira Otto M, Yamal J, Aguilar D, Gunther M, Mofleh D, Harris R, John J, de Vries P, Ramphul R, Serbo D, Kiger J, Banerjee D, Bonvino N, Merchant A, Clifford W, Mikhail J, Xu H, Murphy R, Wei Q, Vahidy F, Morrison A, Boerwinkle E. Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality. PLOS ONE 2021, 16: e0247235. PMID: 34081724, PMCID: PMC8174716, DOI: 10.1371/journal.pone.0247235.Peer-Reviewed Original ResearchConceptsBody mass indexCOVID-19 patientsRisk factorsTobacco useCOVID-19 fatalitiesHealth information exchangeRace/ethnicityCOVID-19Laboratory risk factorsNumber of comorbiditiesCOVID-19 cohortMultivariable logistic regressionImportant risk factorPotential risk factorsCOVID-19 outcomesFormer tobacco usersTobacco use historyLarge health information exchangeMass indexElectronic health record systemsUnfavorable outcomeClinical dataTobacco usersOutcome analysisElectronic health informationPrivacy-protecting, reliable response data discovery using COVID-19 patient observations
Kim J, Neumann L, Paul P, Day M, Aratow M, Bell D, Doctor J, Hinske L, Jiang X, Kim K, Matheny M, Meeker D, Pletcher M, Schilling L, SooHoo S, Xu H, Zheng K, Ohno-Machado L, Anderson D, Anderson N, Balacha C, Bath T, Baxter S, Becker-Pennrich A, Bernstam E, Carter W, Chau N, Choi Y, Covington S, DuVall S, El-Kareh R, Florian R, Follett R, Geisler B, Ghigi A, Gottlieb A, Hu Z, Ir D, Knight T, Koola J, Kuo T, Lee N, Mansmann U, Mou Z, Murphy R, Neumann L, Nguyen N, Niedermayer S, Park E, Perkins A, Post K, Rieder C, Scherer C, Soares A, Soysal E, Tep B, Toy B, Wang B, Wu Z, Zhou Y, Zucker R. Privacy-protecting, reliable response data discovery using COVID-19 patient observations. Journal Of The American Medical Informatics Association 2021, 28: 1765-1776. PMID: 34051088, PMCID: PMC8194878, DOI: 10.1093/jamia/ocab054.Peer-Reviewed Original ResearchCOVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model
Wang J, Abu-El-Rub N, Gray J, Pham H, Zhou Y, Manion F, Liu M, Song X, Xu H, Rouhizadeh M, Zhang Y. COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model. Journal Of The American Medical Informatics Association 2021, 28: 1275-1283. PMID: 33674830, PMCID: PMC7989301, DOI: 10.1093/jamia/ocab015.Peer-Reviewed Original ResearchConceptsNatural language processing toolsCommon data modelLanguage processing toolsElectronic health recordsClinical natural language processing toolsData modelDeep learning-based modelProcessing toolsOMOP Common Data ModelPattern-based rulesObservational Medical Outcomes Partnership Common Data ModelLearning-based modelsSpecific information needsUse casesNLP toolsClinical textFree textExtensive evaluationDownloadable packageInformation needsHybrid approachResearch communityHealth recordsData sourcesHigh performance
2020
How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles
Zuo X, Chen Y, Ohno-Machado L, Xu H. How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles. Briefings In Bioinformatics 2020, 22: 800-811. PMID: 33757278, PMCID: PMC7799277, DOI: 10.1093/bib/bbaa331.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCOVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes
Dong X, Li J, Soysal E, Bian J, DuVall S, Hanchrow E, Liu H, Lynch K, Matheny M, Natarajan K, Ohno-Machado L, Pakhomov S, Reeves R, Sitapati A, Abhyankar S, Cullen T, Deckard J, Jiang X, Murphy R, Xu H. COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes. Journal Of The American Medical Informatics Association 2020, 27: 1437-1442. PMID: 32569358, PMCID: PMC7337837, DOI: 10.1093/jamia/ocaa145.Peer-Reviewed Original ResearchConceptsElectronic health recordsLOINC codesSecondary useRule-based toolOnline web applicationOpen-source packageCritical data elementsWeb applicationData networksEnd usersData elementsIndependent test setHealth recordsTest setKey challengesData normalizationCritical resourcesTest namesRoutine clinical practice dataCodeClinical practice dataCoronavirus disease 2019COVID-19 diagnostic testsToolDevelopersCoronavirus: indexed data speed up solutions
Ohno-Machado L, Xu H. Coronavirus: indexed data speed up solutions. Nature 2020, 584: 192-192. PMID: 32782375, DOI: 10.1038/d41586-020-02331-3.Commentaries, Editorials and Letters