2024
Explainable AI for Fair Sepsis Mortality Predictive Model
Chang C, Wang X, Yang C. Explainable AI for Fair Sepsis Mortality Predictive Model. Lecture Notes In Computer Science 2024, 14845: 267-276. DOI: 10.1007/978-3-031-66535-6_29.Peer-Reviewed Original ResearchTransfer learning processFairness of model predictionsExplainable AIExplainability methodsAI applicationsPrediction modelArtificial intelligenceImportance algorithmTransform clinical decision-makingExplainabilityDiverse patient demographicsFairnessHealthcare stakeholdersPredictive performanceLearning processMitigate biasAlgorithmHealthcare deliveryDecision-makingIntelligenceClinical decision-makingHealthcare professionalsMortality prediction modelMethodHealthcare
2023
Development and Validation of the Life Expectancy Estimator for Older Adults with Diabetes (LEAD): the Diabetes and Aging Study
Karter A, Parker M, Moffet H, Lipska K, Laiteerapong N, Grant R, Lee C, Huang E. Development and Validation of the Life Expectancy Estimator for Older Adults with Diabetes (LEAD): the Diabetes and Aging Study. Journal Of General Internal Medicine 2023, 38: 2860-2869. PMID: 37254010, PMCID: PMC10228886, DOI: 10.1007/s11606-023-08219-y.Peer-Reviewed Original ResearchOlder adultsLife expectancyKaiser Permanente Northern CaliforniaRisk score toolLimited life expectancyClinical trial inclusionMortality prediction modelElectronic health recordsOlder patientsPatient characteristicsRetrospective cohortTrial inclusionAdvanced careCancer screeningClinical trialsTreatment goalsDiabetesSurvival analysisSurvival curvesLife expectancy modelCohortVital signsAging StudyHealth recordsKey ResultsIn
2022
Multimorbidity measures differentially predicted mortality among older Chinese adults
Yao SS, Xu HW, Han L, Wang K, Cao GY, Li N, Luo Y, Chen YM, Su HX, Chen ZS, Huang ZT, Hu YH, Xu B. Multimorbidity measures differentially predicted mortality among older Chinese adults. Journal Of Clinical Epidemiology 2022, 146: 97-105. PMID: 35259446, DOI: 10.1016/j.jclinepi.2022.03.002.Peer-Reviewed Original ResearchConceptsNet reclassification indexIntegrated discrimination improvementMultimorbidity measuresMortality prediction modelOlder Chinese adultsMultimorbidity patternsC-statisticCondition countMortality predictionChinese adultsMultimorbidity trajectoriesNet reclassification improvementCox regressionReclassification improvementReclassification indexChronic conditionsDiscrimination improvementMortality riskHigh riskIntegrated discriminationMultimorbidityStudy designMortalityAdultsAge
2021
COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal
Bottino F, Tagliente E, Pasquini L, Di Napoli A, Lucignani M, Figà-Talamanca L, Napolitano A. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. Journal Of Personalized Medicine 2021, 11: 893. PMID: 34575670, PMCID: PMC8467935, DOI: 10.3390/jpm11090893.Peer-Reviewed Original ResearchSystematic reviewMortality predictionMachine learning techniquesClinical decision makingRisk of deathClinical decision-makingIntensive care unit admissionMachine learning methodsMortality prediction modelMortality outcomesCOVID patientsLearning techniquesLearning methodsIntensive care unitLength of hospital stayBaseline machine learning techniquesUnit admissionCare unitPublished literatureOutcomesDeep learningAdmissionHospital stayImminent risk of deathMachine learning
2018
Shock Index Predicts Patient‐Related Clinical Outcomes in Stroke
Myint P, Sheng S, Xian Y, Matsouaka R, Reeves M, Saver J, Bhatt D, Fonarow G, Schwamm L, Smith E. Shock Index Predicts Patient‐Related Clinical Outcomes in Stroke. Journal Of The American Heart Association 2018, 7: e007581. PMID: 30371191, PMCID: PMC6222962, DOI: 10.1161/jaha.117.007581.Peer-Reviewed Original ResearchConceptsShock indexClinical outcomesWorse outcomesPatient-related clinical outcomesHealth Stroke ScaleBlood pressure componentsAcute stroke casesRankin Scale scoreSystolic blood pressureUseful prognostic indicatorMortality prediction modelIndividual stroke subtypesLinear spline modelsHospital mortalityHospital outcomesHospital stayStroke ScaleAcute strokeDischarge destinationBlood pressureStroke subtypesPoint of carePoor outcomePrognostic valueStroke cases
2012
Should the IDC-9 Trauma Mortality Prediction Model become the new paradigm for benchmarking trauma outcomes?
Haider A, Villegas C, Saleem T, Efron D, Stevens K, Oyetunji T, Cornwell E, Bowman S, Haack S, Baker S, Schneider E. Should the IDC-9 Trauma Mortality Prediction Model become the new paradigm for benchmarking trauma outcomes? Journal Of Trauma And Acute Care Surgery 2012, 72: 1695-1701. PMID: 22695443, DOI: 10.1097/ta.0b013e318256a010.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBenchmarkingCohort StudiesDatabases, FactualFemaleHospital MortalityHumansInternational Classification of DiseasesMaleMiddle AgedModels, StatisticalPredictive Value of TestsRetrospective StudiesSensitivity and SpecificityTrauma Severity IndicesTreatment OutcomeUnited StatesWounds and InjuriesYoung AdultConceptsInjury Severity ScoreNew ISSMortality prediction modelTrauma Mortality Prediction ModelTrauma outcomesInjury typeInjury severityNational Trauma Data BankNew Injury Severity ScoreInjury severity indicesMortality prediction abilityTrauma Data BankTrauma registry dataCrude mortality rateReceiver operator characteristic curveDRG International ClassificationOperator characteristic curveHospital mortalitySeverity scoreSubgroup analysisRegistry dataRetrospective analysisOutcome measuresPrognostic studiesInternational Classification
2007
Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality
Matheny M, Resnic F, Arora N, Ohno-Machado L. Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality. Journal Of Biomedical Informatics 2007, 40: 688-697. PMID: 17600771, PMCID: PMC2170520, DOI: 10.1016/j.jbi.2007.05.008.Peer-Reviewed Original ResearchConceptsSupport vector machineRadial Basis Kernel Support Vector MachineKernel support vector machineCross-entropy errorSVM parameter optimizationUnseen test dataSVM kernel typesTraining dataVector machineEvolutionary algorithmGrid searchMean squared errorKernel typeMachineOptimization methodPrediction modelNumber of methodsParameter optimizationTest dataMedical applicationsOptimization parametersMortality prediction modelAlgorithmBest modelApplications
2006
PROGNOSIS IN CRITICAL CARE
Ohno-Machado L, Resnic F, Matheny M. PROGNOSIS IN CRITICAL CARE. Annual Review Of Biomedical Engineering 2006, 8: 567-599. PMID: 16834567, DOI: 10.1146/annurev.bioeng.8.061505.095842.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
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