2025
Detection of emergency department patients at risk of dementia through artificial intelligence
Cohen I, Taylor R, Xue H, Faustino I, Festa N, Brandt C, Gao E, Han L, Khasnavis S, Lai J, Mecca A, Sapre A, Young J, Zanchelli M, Hwang U. Detection of emergency department patients at risk of dementia through artificial intelligence. Alzheimer's & Dementia 2025, 21: e70334. PMID: 40457744, PMCID: PMC12130574, DOI: 10.1002/alz.70334.Peer-Reviewed Original ResearchConceptsElectronic health record dataHealth record dataEmergency departmentDetect dementiaDementia detectionYale New Haven HealthRecord dataRisk of dementiaEmergency department patientsBalance detection accuracyDementia algorithmsImprove patient outcomesCare coordinationCare transitionsDementia diagnosisReal-time applicationsClinical decision-makingClinician supportED usePatient safetyProbable dementiaMachine learning algorithmsED workflowED visitsED encountersAcoustic-based machine learning approaches for depression detection in Chinese university students
Wei Y, Qin S, Liu F, Liu R, Zhou Y, Chen Y, Xiong X, Zheng W, Ji G, Meng Y, Wang F, Zhang R. Acoustic-based machine learning approaches for depression detection in Chinese university students. Frontiers In Public Health 2025, 13: 1561332. PMID: 40443925, PMCID: PMC12119278, DOI: 10.3389/fpubh.2025.1561332.Peer-Reviewed Original ResearchConceptsPatient Health Questionnaire-9Mel-frequency cepstral coefficientsLinear discriminant analysisMachine learning algorithmsAcoustic featuresLearning algorithmsIdentification of depressionMonitoring of depressionCross-sectional studyGlobal public health problemSHapley Additive exPlanationsDepression screeningSelf-report methodsPublic health problemIdentifying DepressionLinear discriminant analysis modelDepression assessmentSupport vector classificationAutomated identificationMachine learning approachArea under the curveHealth problemsOpenSMILE toolkitLogistic regressionCepstral coefficientsSelection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real‐world data and machine learning model algorithms
Tang H, Xia H, Sun N, Hernandez P, Wang M, Adeniran A, Cai G. Selection of neuroendocrine markers in diagnostic workup of neuroendocrine neoplasms: The real‐world data and machine learning model algorithms. Cancer Cytopathology 2025, 133: e70018. PMID: 40289395, DOI: 10.1002/cncy.70018.Peer-Reviewed Original ResearchConceptsMachine learning algorithmsReal-world dataLearning algorithmsNeural networkRandom forestNeural network modelNeuroendocrine neoplasmsAUC-ROCMachine learning modelsNeuroendocrine markersDiagnostic workupLearning modelsMachine learning modeling algorithmsNetwork modelDiagnosis of neuroendocrine neoplasmsModeling algorithmAlgorithmArea under the curveMachineArea under the curve of receiver operating characteristic curvesReceiver operating characteristic curveNetworkNEC casesCytology casesNon-NENsPredicting conversion to psychosis using machine learning: response to Cannon
Smucny J, Cannon T, Bearden C, Addington J, Cadenhead K, Cornblatt B, Keshavan M, Mathalon D, Perkins D, Stone W, Walker E, Woods S, Davidson I, Carter C. Predicting conversion to psychosis using machine learning: response to Cannon. Frontiers In Psychiatry 2025, 15: 1520173. PMID: 39882161, PMCID: PMC11775650, DOI: 10.3389/fpsyt.2024.1520173.Peer-Reviewed Original ResearchMachine learning algorithmsMachine learning modelsLearning algorithmsConversion to psychosisMachine learningLearning modelsStandard machine learning algorithmsClinical high riskNAPLS-2Overall performanceNaive BayesModel generalizationClinical high-risk individualsPredicting conversion to psychosisTest setIndependent datasetsRandom forest methodDataset
2024
Evaluating the usability of an HIV-prevention artificial intelligence chatbot in Malaysia: national observational study.
Ni Z, Oh S, Saifi R, Altice F, Azwa I. Evaluating the usability of an HIV-prevention artificial intelligence chatbot in Malaysia: national observational study. JMIR Human Factors 2024 PMID: 40439677, DOI: 10.2196/70034.Peer-Reviewed Original ResearchHIV self-test kitsPromote HIV testingSelf-test kitsHIV testingAI chatbotsObservational studyMSM-friendly clinicsFree HIV self-testing kitArtificial intelligenceDevelopment platformMental health servicesMiddle-income countriesPersonal health informationHuman-chatbot interactionsUpper middle-income countriesNational observational studyMachine learning algorithmsHealth servicesArtificial intelligence chatbotsHealth informationMental healthCommunity outreachSocial networking appsHealthcare systemHIV epidemicArtificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model
Alexiou V, Sumpio B, Vassiliou A, Kakkos S, Geroulakos G. Artificial Intelligence in Diagnosing and Managing Vascular Surgery Patients: An Experimental Study Using the GPT-4 Model. Annals Of Vascular Surgery 2024, 111: 260-267. PMID: 39586530, DOI: 10.1016/j.avsg.2024.11.014.Peer-Reviewed Original ResearchNatural language processingAI modelsArtificial intelligenceMachine learning algorithmsLanguage modelLearning algorithmsVascular surgery patientsRelevant answersLanguage processingAI chatbotsIntroduction of artificial intelligenceStandalone solutionMedical classification systemsTest scenariosSurgery patientsMedical informationClinical scenariosComplex problemsIntelligenceScientific fieldsComplex clinical scenariosScenariosStatistically significant differenceClinically relevant answersPerformance variationUsing Voice Data to Facilitate Depression Risk Assessment in Primary Health Care
Goyal A, Man R, Lee R, Saha K, Altice F, Poellabauer C, Papakyriakopoulos O, Cheung L, De Choudhury M, Allagh K, Kumar N. Using Voice Data to Facilitate Depression Risk Assessment in Primary Health Care. 2024, 17-18. DOI: 10.1145/3630744.3658408.Peer-Reviewed Original ResearchDepression riskPrimary health careDepression risk assessmentLower-income patientsHealth careTreating depressionVoice dataDepressionK-nearest neighbor classifierMachine learning algorithmsCollected voice dataRiskNeighbor classifierLearning algorithmsStable internet connectionK-nearestInternet connectionTelehealthCareA roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness
Kidwai-Khan F, Wang R, Skanderson M, Brandt C, Fodeh S, Womack J. A roadmap to artificial intelligence (AI): Methods for designing and building AI ready data to promote fairness. Journal Of Biomedical Informatics 2024, 154: 104654. PMID: 38740316, PMCID: PMC11144439, DOI: 10.1016/j.jbi.2024.104654.Peer-Reviewed Original ResearchArtificial intelligenceMachine learningNatural language processing techniquesRaw dataLife cycle of dataLanguage processing techniquesInput dataApplication of artificial intelligenceArtificial intelligence processesMachine learning algorithmsTransform raw dataNatural language processing algorithmsArtificial intelligence methodsApplication of AILanguage processing algorithmsLearning algorithmsIntelligent processingError rateIntelligence methodsData governanceProcessing algorithmsData expertiseAlgorithmic biasElectronic health record dataData frameworksIn vivo neuropil density from anatomical MRI and machine learning
Akif A, Staib L, Herman P, Rothman D, Yu Y, Hyder F. In vivo neuropil density from anatomical MRI and machine learning. Cerebral Cortex 2024, 34: bhae200. PMID: 38771239, PMCID: PMC11107380, DOI: 10.1093/cercor/bhae200.Peer-Reviewed Original ResearchConceptsMagnetic resonance imagingSynaptic densityNeuropil densityCellular densityArtificial neural networkNeural networkPositron emission tomographyAnatomical magnetic resonance imagingHealthy subjectsSynaptic activityMRI scansMachine learning algorithmsBrain's energy budgetEmission tomographyIn vivo MRI scansResonance imagingTissue cellularityLearning algorithmsDiffusion magnetic resonance imagingMachine learningMicroscopic interpretationInterpretation of functional neuroimaging dataIndividual predictionsSubjectsMachine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade
Jiang Z, Carlson B, Deiana A, Eastlack J, Hauck S, Hsu S, Narayan R, Parajuli S, Yin D, Zuo B. Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade. Journal Of Instrumentation 2024, 19: p05031. DOI: 10.1088/1748-0221/19/05/p05031.Peer-Reviewed Original ResearchProcessing tasksMachine learningComplexity of algorithm designIndividuals process tasksSignal processing tasksVolume of dataReal-time processingMachine learning algorithmsMachine learning evaluationLearning algorithmsOverall latencyFiltering decisionsFiltering taskATLAS experimentAlgorithm designEvent processorProcessing platformHigh energy physics applicationsFPGALarge Hadron ColliderAlgorithmResource utilizationMachineTaskHadron ColliderMultimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data
Batta I, Abrol A, Calhoun V, Initiative A. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. Journal Of Neuroscience Methods 2024, 406: 110109. PMID: 38494061, PMCID: PMC11100582, DOI: 10.1016/j.jneumeth.2024.110109.Peer-Reviewed Original ResearchConceptsBrain subspacesStandard machine learning algorithmsHigh-dimensional neuroimaging dataTrain machine learning modelsUnsupervised decompositionMachine learning algorithmsMachine learning modelsFunctional sub-systemsSubspace analysisSubspace componentsLearning algorithmsSupervised approachBiological traitsLearning modelsSub-systemsAlzheimer's diseaseSubspaceComputational frameworkActive subspaceCross-validation procedureNeuroimaging dataAD-related brain regionsAutomated identificationPredictive performanceOrientation subspacesDifferentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study
Shiri I, Salimi Y, Saberi A, Pakbin M, Hajianfar G, Avval A, Sanaat A, Akhavanallaf A, Mostafaei S, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khosravi B, Khateri M, Bijari S, Atashzar M, Shayesteh S, Babaei M, Jenabi E, Hasanian M, Shahhamzeh A, Ghomi S, Mozafari A, Shirzad‐Aski H, Movaseghi F, Bozorgmehr R, Goharpey N, Abdollahi H, Geramifar P, Radmard A, Arabi H, Rezaei‐Kalantari K, Oveisi M, Rahmim A, Zaidi H. Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study. International Journal Of Imaging Systems And Technology 2024, 34 DOI: 10.1002/ima.23028.Peer-Reviewed Original ResearchRecursive feature eliminationClassifier combinationMachine learningRandom forestEffective machine learningDimensionality reduction techniquesMachine learning algorithmsLearning-based modelsMachine learning-based modelsHigh performancePublic datasetsLearning algorithmsBalanced classesCOVID-19 pneumoniaFeature eliminationMulti-centre datasetTest setLung radiomics featuresDatasetReduction techniquesCOVID-19 patientsMachineCross-validationLung diseaseRadiomic featuresAchieving Equity via Transfer Learning With Fairness Optimization
Wang X, Chang C, Yang C. Achieving Equity via Transfer Learning With Fairness Optimization. IEEE Access 2024, 12: 195229-195241. DOI: 10.1109/access.2024.3519465.Peer-Reviewed Original ResearchBias mitigation approachesFairness optimizationTransfer learningReal-world datasetsMachine learning algorithmsMachine learning modelsDecision-making systemMinimal performance degradationFairness enhancementFairness constraintsAccurate classifierLearning algorithmsAI systemsTraining processMitigation approachesLearning modelsTrade-OffsPerformance degradationPerformance impactSuperior fairnessPerformance optimizationFairnessPredictive performanceLearningMachineChapter 2 Data access, data bias, data equity
Shung D, Laine L. Chapter 2 Data access, data bias, data equity. 2024, 13-26. DOI: 10.1016/b978-0-323-95068-8.00002-9.Peer-Reviewed Original ResearchMachine learning from real data: A mental health registry case study
Gentili E, Franchini G, Zese R, Alberti M, Ferrara M, Domenicano I, Grassi L. Machine learning from real data: A mental health registry case study. Computer Methods And Programs In Biomedicine Update 2024, 5: 100132. DOI: 10.1016/j.cmpbup.2023.100132.Peer-Reviewed Original ResearchClassification taskDifferent Machine Learning algorithmsMachine learning algorithmsMachine learning techniquesReal-world datasetsCost-sensitive learningImbalanced classification tasksSynthetic minority oversampling techniqueMinority oversampling techniqueFeature selection analysisLearning techniquesElectronic health recordsAlgorithm levelMedical domainLearning algorithmData imbalanceRandom undersamplingOversampling techniqueHealth recordsReal dataHealthcare fieldOriginal dataDatasetTaskBest setting
2023
Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital
Li X, Liu W, Kong W, Zhao W, Wang H, Tian D, Jiao J, Yu Z, Liu S. Prediction of outpatient waiting time: using machine learning in a tertiary children’s hospital. Translational Pediatrics 2023, 12: 2030043-2032043. PMID: 38130586, PMCID: PMC10730972, DOI: 10.21037/tp-23-58.Peer-Reviewed Original ResearchMachine learning algorithmsLearning algorithmsOutpatient waiting timesMachine learningPediatric hospitalEnhance healthcare servicesGBDT modelDepartment categoryPatient-centred careWaiting timeEffective hospital managementClassification methodWaiting time of patientsTertiary children's hospitalHealthcare servicesMedical appointmentsOptimization modelAlgorithmPredicted waiting timeHospital managementPatient anxietyMachineChildren's HospitalOutpatient clinicDay of admissionSystematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction
Lost J, Verma T, Jekel L, von Reppert M, Tillmanns N, Merkaj S, Petersen G, Bahar R, Gordem A, Haider M, Subramanian H, Brim W, Ikuta I, Omuro A, Conte G, Marquez-Nostra B, Avesta A, Bousabarah K, Nabavizadeh A, Kazerooni A, Aneja S, Bakas S, Lin M, Sabel M, Aboian M. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction. American Journal Of Neuroradiology 2023, 44: 1126-1134. PMID: 37770204, PMCID: PMC10549943, DOI: 10.3174/ajnr.a8000.Peer-Reviewed Original ResearchConceptsData setsMachine learning algorithmsValidation data setsExternal validation data setsDifferent machineLearning algorithmLearning Product Rankings Robust to Fake Users
Golrezaei N, Manshadi V, Schneider J, Sekar S. Learning Product Rankings Robust to Fake Users. Operations Research 2023, 71: 1171-1196. DOI: 10.1287/opre.2022.2380.Peer-Reviewed Original ResearchFake usersOnline learning algorithmLearning algorithmsProduct rankingDetect fake usersEfficient learning algorithmClick farmingImplementing multiple levelsMachine learning algorithmsE-commerce platformsFraudulent behaviorFraudulent usersSuboptimal rankingsUser feedbackCorrupted dataData analyticsFraudulent actorsE-commerceOptimal rankingOnline platformsUsersTD managementDisplay orderLearning methodologyAlgorithmStroke Prediction Using Machine Learning
D M A, Kumar A, Teja G, Sukesh M, S D, Chauhan N, Oviya I, Raja K. Stroke Prediction Using Machine Learning. 2023, 00: 1-5. DOI: 10.1109/icaecc59324.2023.10560191.Peer-Reviewed Original Research
2022
COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients
Shiri I, Salimi Y, Pakbin M, Hajianfar G, Avval A, Sanaat A, Mostafaei S, Akhavanallaf A, Saberi A, Mansouri Z, Askari D, Ghasemian M, Sharifipour E, Sandoughdaran S, Sohrabi A, Sadati E, Livani S, Iranpour P, Kolahi S, Khateri M, Bijari S, Atashzar M, Shayesteh S, Khosravi B, Babaei M, Jenabi E, Hasanian M, Shahhamzeh A, Foroghi Ghomi S, Mozafari A, Teimouri A, Movaseghi F, Ahmari A, Goharpey N, Bozorgmehr R, Shirzad-Aski H, Mortazavi R, Karimi J, Mortazavi N, Besharat S, Afsharpad M, Abdollahi H, Geramifar P, Radmard A, Arabi H, Rezaei-Kalantari K, Oveisi M, Rahmim A, Zaidi H. COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients. Computers In Biology And Medicine 2022, 145: 105467. PMID: 35378436, PMCID: PMC8964015, DOI: 10.1016/j.compbiomed.2022.105467.Peer-Reviewed Original ResearchConceptsFeature selectorArea under the receiver operating characteristic curveCT radiomics featuresDeep learning-based modelMachine learning algorithmsRadiomic featuresLearning-based modelsCOVID-19 patientsCross-validation strategyRadiomics modelLearning algorithmsSelection algorithmPrognostic modelCT-based radiomics modelRF classifierHeterogeneous datasetsHigh performanceCT radiomics modelRT-PCR positive casesReceiver operating characteristic curveTest datasetTest setDatasetLung CT radiomics featuresWhole-lung segmentation
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