2025
Artificial Intelligence: Crossing a Threshold in Healthcare Education and Simulation
Bajwa M, Morton A, Patel A, Palaganas J, Gross I. Artificial Intelligence: Crossing a Threshold in Healthcare Education and Simulation. Cureus Journal Of Computer Science 2025, 2: 3758. DOI: 10.7759/s44389-025-03758-3.Peer-Reviewed Original ResearchPreparing for The Silver Tsunami: The Potential for use of Big Data and Artificial Intelligence in Geriatric Anesthesia
Chu L, Kurup V. Preparing for The Silver Tsunami: The Potential for use of Big Data and Artificial Intelligence in Geriatric Anesthesia. Current Anesthesiology Reports 2025, 15: 17. DOI: 10.1007/s40140-024-00674-5.Peer-Reviewed Original ResearchBig data analyticsData analyticsMachine learningPotential of big data analyticsAdoption of MLIntegration of MLData privacyBig dataData scientistsData-driven insightsArtificial intelligenceGeriatric anesthesiaAlgorithmic biasModel interpretationML modelsPreoperative risk stratificationPredicting postoperative complicationsIntraoperative anesthesia managementPostoperative complicationsRespiratory complicationsRisk stratificationElderly patientsDataAdverse outcomesAnesthesia management
2024
Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes
Komrokji R, Lanino L, Ball S, Bewersdorf J, Marchetti M, Maggioni G, Travaglino E, Al Ali N, Fenaux P, Platzbecker U, Santini V, Diez-Campelo M, Singh A, Jain A, Aguirre L, Tinsley-Vance S, Schwabkey Z, Chan O, Xie Z, Brunner A, Kuykendall A, Bennett J, Buckstein R, Bejar R, Carraway H, DeZern A, Griffiths E, Halene S, Hasserjian R, Lancet J, List A, Loghavi S, Odenike O, Padron E, Patnaik M, Roboz G, Stahl M, Sekeres M, Steensma D, Savona M, Taylor J, Xu M, Sweet K, Sallman D, Nimer S, Hourigan C, Wei A, Sauta E, D’Amico S, Asti G, Castellani G, Delleani M, Campagna A, Borate U, Sanz G, Efficace F, Gore S, Kim T, Daver N, Garcia-Manero G, Rozman M, Orfao A, Wang A, Foucar M, Germing U, Haferlach T, Scheinberg P, Miyazaki Y, Iastrebner M, Kulasekararaj A, Cluzeau T, Kordasti S, van de Loosdrecht A, Ades L, Zeidan A, Della Porta M, Syndromes I. Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes. The Lancet Haematology 2024, 11: e862-e872. PMID: 39393368, DOI: 10.1016/s2352-3026(24)00251-5.Peer-Reviewed Original ResearchGenomic featuresData-driven approachTP53 inactivationGenomic heterogeneityEntity labelsGenetic featuresDel(7q)/-7Myelodysplastic syndromeGenomic profilingData scientistsMutated SF3B1Cluster assignmentComplex karyotypeRUNX1 mutationsModified Delphi consensus processDel(5qIsolated del(5qAcute myeloid leukemiaData-DrivenDelphi consensus processMarrow blasts
2021
COVID-19 Pandemic in India: Through the Lens of Modeling
Babu G, Ray D, Bhaduri R, Halder A, Kundu R, Menon G, Mukherjee B. COVID-19 Pandemic in India: Through the Lens of Modeling. Global Health Science And Practice 2021, 9: 220-228. PMID: 34234020, PMCID: PMC8324184, DOI: 10.9745/ghsp-d-21-00233.Peer-Reviewed Original Research
2020
A Prototype Application to Identify LGBT Patients in Clinical Notes
Workman T, Goulet J, Brandt C, Skanderson M, Wang R, Warren A, Eleazer J, Gordon K, Zeng-Treitler Q. A Prototype Application to Identify LGBT Patients in Clinical Notes. 2020, 00: 4270-4275. DOI: 10.1109/bigdata50022.2020.9378109.Peer-Reviewed Original ResearchElectronic health record notesPrototype applicationLGBT patientsRule-based patternLarge data sourcesBinary classification taskRecord notesData scientistsMachine learningClassification taskPositive predictive valueData researchData sourcesTest setClinical relevancePredictive valueHealthcare providersPatientsClinical notesHealth disparitiesLittle workDisproportional burdenApplicationsTaskLearningBringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations
Comess S, Akbay A, Vasiliou M, Hines RN, Joppa L, Vasiliou V, Kleinstreuer N. Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations. Frontiers In Artificial Intelligence 2020, 3: 31. PMID: 33184612, PMCID: PMC7654840, DOI: 10.3389/frai.2020.00031.Peer-Reviewed Original ResearchBig dataArtificial InteligenceSkilled data scientistsComplex data setsData scientistsScientific computingCyber infrastructureMachine learningAI approachesReusable dataFAIR principlesData curationComputational toolsData setsEnvironmental public health researchResearch hubData collectionActionable recommendationsBroader public health communityComputingSharingInteligenceSufficient informationCurationParamount importance
2016
Protecting genomic data analytics in the cloud: state of the art and opportunities
Tang H, Jiang X, Wang X, Wang S, Sofia H, Fox D, Lauter K, Malin B, Telenti A, Xiong L, Ohno-Machado L. Protecting genomic data analytics in the cloud: state of the art and opportunities. BMC Medical Genomics 2016, 9: 63. PMID: 27733153, PMCID: PMC5062944, DOI: 10.1186/s12920-016-0224-3.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsHuman genomic dataSecure computation techniquesPublic cloud environmentSecure computation methodsGenomic data analyticsReal-world environmentsSecond Critical AssessmentSecure outsourcingCloud environmentCryptographic technologyPublic cloudSecure collaborationUnauthorized usersComputation tasksData privacyData analyticsBiomedical computingData scientistsComputational environmentGenomic dataWorld environmentComputation techniquesMultiple organizationsPractical algorithmPrivacy
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