2025
A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022
Xu Q, Li L, Wei Z, Lu X, Wei N, Lee X, Dai Y. A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022. Agricultural And Forest Meteorology 2025, 372: 110645. DOI: 10.1016/j.agrformet.2025.110645.Peer-Reviewed Original ResearchET datasetsET productsShort temporal coverageLand surface modelsCoarse spatial resolutionIn situ observationsIn situ measurementsFlux tower observationsAtmospheric forcingReanalysis dataEvapotranspiration datasetsRegional hydrologyHydrological fluxesGeoscience datasetsLand surfaceTower observationsSpatiotemporal variabilityTemporal coverageCarbon cycleRemote sensingWet regionsAncillary dataCycle applicationsMachine learningSurface modelDesign-based provider profiling with artificial intelligence to enhance quality and equity in health care delivery
Wu W, Díaz I, Horwitz L. Design-based provider profiling with artificial intelligence to enhance quality and equity in health care delivery. Health Services And Outcomes Research Methodology 2025, 1-15. DOI: 10.1007/s10742-025-00355-8.Peer-Reviewed Original ResearchArtificial intelligenceHealth care deliveryProvider profilingCare deliveryEffectiveness of health care deliveryNatural language processingData privacy concernsHealth care qualityPrivacy concernsAlgorithmic disparitiesLanguage processingMachine learningCare qualityPerformance benchmarksInflexible modelsProvidersProfile objectsIntelligenceHealthCost-effectiveInadequate attentionDeliveryEvidence-based accountBenchmarksExtracting antibiotic susceptibility from free-text microbiology reports using natural language processing.
Chou A, Hauser R, Bastian L, Brandt C, Trautner B. Extracting antibiotic susceptibility from free-text microbiology reports using natural language processing. Infection Control And Hospital Epidemiology 2025, 1-3. PMID: 40740000, DOI: 10.1017/ice.2025.10210.Peer-Reviewed Original ResearchImproving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation
Dehkordi M, Perl Y, Deek F, He Z, Keloth V, Liu H, Elhanan G, Einstein A. Improving Large Language Models’ Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation. JMIR Medical Informatics 2025, 13: e66476. PMID: 40705416, PMCID: PMC12332456, DOI: 10.2196/66476.Peer-Reviewed Original ResearchConceptsElectronic health recordsSummarization accuracyContents of Electronic Health RecordsElectronic health record notesText summarizationLanguage modelEvaluation metricsMachine learningInterface terminologyMIMIC-III databaseDischarge notesSimplification stepsHealth recordsErroneous informationComparative evaluationAccuracyInformationSummarizationHeaderLanguageAmerican Medical AssociationA systematic review of computational modeling of interpersonal dynamics in psychopathology
Zavlis O, Story G, Friedrich C, Fonagy P, Moutoussis M. A systematic review of computational modeling of interpersonal dynamics in psychopathology. Nature Mental Health 2025, 3: 932-942. DOI: 10.1038/s44220-025-00465-9.Peer-Reviewed Original ResearchInterpersonal dynamicsTreatment of mental health problemsMental health problemsMental health conditionsInterpersonal disruptionsHyper-mentalizingMood conditionPsychotic conditionsPsychopathologyMental healthSocial learningSocial underpinningsRelational dynamicsSystematic reviewComprehensive performance metricsHealth problemsSystematic assessmentMoodAutismHealth conditionsPersonal conditionsLearningReinforcement learningComputational modelMachine learningTwo-stage interrupted time series analysis with machine learning: evaluating the health effects of the 2018 wildfire smoke event in San Francisco County as a case study
Dey A, Ma Y, Carrasco-Escobar G, Han C, Rerolle F, Benmarhnia T. Two-stage interrupted time series analysis with machine learning: evaluating the health effects of the 2018 wildfire smoke event in San Francisco County as a case study. American Journal Of Epidemiology 2025, kwaf147. PMID: 40663094, DOI: 10.1093/aje/kwaf147.Peer-Reviewed Original ResearchNeural network autoregressionPost-event periodCase studyIT frameworkRandomized Controlled TrialsInterrupted time seriesNatural experimentSan Francisco CountyIdentification strategyEthical issuesSmoke eventsWildfire smoke eventsCounterfactual scenariosMachine learning algorithmsRespiratory hospitalizationsInterrupted time series analysisWeather eventsHealth policy evaluationCausal effectsHyperparameter tuningLearning algorithmsTime series analysisQuasi-experimental designMachine learningPolicy evaluationMutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia
Du Y, Wang Z, Niu J, Wang Y, Pearlson G, Calhoun V. Mutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia. IEEE Transactions On Medical Imaging 2025, PP: 1-1. PMID: 40614156, DOI: 10.1109/tmi.2025.3585880.Peer-Reviewed Original ResearchNoisy label learningDeep neural networksLabel learningNoisy labelsNoisy label learning methodsMultiple deep neural networksLevels of label noiseBipolar disorderAdvanced deep learning techniquesState-of-the-artDeep learning techniquesTransdiagnostic classificationMental disordersCIFAR-10Label noiseNosology of mental disordersClassification accuracyNeural networkLearning techniquesClassification of bipolar disorderLearning methodsMachine learningFunctional connectivity dataExperimental resultsSZ patientsFalse-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions
Edelson M, Pham A, Kuo T. False-positive tolerant model misconduct mitigation in distributed federated learning on electronic health record data across clinical institutions. Scientific Reports 2025, 15: 23310. PMID: 40603924, PMCID: PMC12223104, DOI: 10.1038/s41598-025-04069-2.Peer-Reviewed Original ResearchConceptsLearning scenariosFederated learning scenarioCollaborative machine learningDecentralized blockchain networkFederated learning processMachine learning algorithmsFederated LearningBlockchain networkBlockchain characteristicsLearning algorithmsSecurity risksMachine learningCharacteristics of transparencyExcessive false alarmsElectronic health record dataFalse alarmsModel integrationCross-institutionalLearning processNetworkRobust approachPredictive health modelLearningCollaborative healthcareScenariosDeep learning in obsessive-compulsive disorder: a narrative review
Zaboski B, Bednarek L, Ayoub K, Pittenger C. Deep learning in obsessive-compulsive disorder: a narrative review. Frontiers In Psychiatry 2025, 16: 1581297. PMID: 40585546, PMCID: PMC12202444, DOI: 10.3389/fpsyt.2025.1581297.Peer-Reviewed Original ResearchDeep learningObsessive-compulsive disorderDeep learning advancesMultimodal datasetWearable sensorsMonitoring solutionsMachine learningLearning advancesObsessive-compulsive disorder researchComplex datasetsDebilitating psychiatric conditionImplementation modelIntrusive thoughtsLearningDatasetPsychiatric conditionsRepetitive behaviorsPrecision psychiatryElectronic medical recordsTreatment response predictionDiagnostic classificationClassificationNarrative reviewTreatment outcomesTreatment predictionQuantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
Smaldone A, Shee Y, Kyro G, Xu C, Vu N, Dutta R, Farag M, Galda A, Kumar S, Kyoseva E, Batista V. Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries. Chemical Reviews 2025, 125: 5436-5460. PMID: 40479601, DOI: 10.1021/acs.chemrev.4c00678.Peer-Reviewed Original ResearchConceptsQuantum machine learningQuantum computationGate-based quantum computersQuantum-classical approachVariational quantum circuitsQuantum neural networkDrug discoveryQuantum circuitsMachine learningContext of drug discoveryMolecular property predictionQuantumMolecular generationProperty predictionNeural networkLearningArtificial intelligence in pediatric intensive care: unlocking integrated monitoring for autonomic nervous system dysregulation
Simms B, Kandil S. Artificial intelligence in pediatric intensive care: unlocking integrated monitoring for autonomic nervous system dysregulation. Pediatric Research 2025, 1-2. PMID: 40437255, DOI: 10.1038/s41390-025-04158-y.Peer-Reviewed Original ResearchEnhanced standardization of clinical T2-weighted prostate images: e-CAMP with T2 prior
Zhang H, Tagare H, Galiana G. Enhanced standardization of clinical T2-weighted prostate images: e-CAMP with T2 prior. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2025 DOI: 10.58530/2025/1680.Peer-Reviewed Original ResearchBig, noisy data: how scalable Gaussian processes can leverage personal weather stations to improve spatiotemporal coverage of urban climate networks
Calhoun Z, Bergin M, Carlson D. Big, noisy data: how scalable Gaussian processes can leverage personal weather stations to improve spatiotemporal coverage of urban climate networks. 2025 DOI: 10.5194/icuc12-491.Peer-Reviewed Original ResearchGaussian process regressionPersonal weather stationsFlexible machine learning techniquesScalable Gaussian processesWeather stationsMachine learning techniquesLow-cost sensorsGaussian processClimate monitoringComplex spatiotemporal dependenciesNearest neighbor Gaussian processLearning techniquesPWS dataLarge datasetsMachine learningNoisy dataScalable approximationDensity of weather stationsSensor placementSensor measurementsSpatiotemporal datasetsUrban climate networkDatasetUrban heat stressProcess regressionVisualizing functional network connectivity differences using an explainable machine-learning method
Sendi M, Itkyal V, Edwards-Swart S, Chun J, Mathalon D, Ford J, Preda A, van Erp T, Pearlson G, Turner J, Calhoun V. Visualizing functional network connectivity differences using an explainable machine-learning method. Physiological Measurement 2025, 46: 045009. PMID: 40245920, DOI: 10.1088/1361-6579/adce52.Peer-Reviewed Original ResearchConceptsCognitive control networkFunctional network connectivitySubcortical networksSHapley Additive exPlanationsExplainable machine learningMachine learning modelsStatistical learning approachResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingClassification accuracyLack interpretabilityMachine-learning methodsMachine learningSynthetic dataLearning approachLearning modelsNetwork connectivityAging AdultsRandom forestOlder aged adultsNetworkSomatomotor networkConnectivity differencesNeural mechanismsCatBoost modelPreventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse
Simson J, Draxler F, Mehr S, Kern C. Preventing Harmful Data Practices by using Participatory Input to Navigate the Machine Learning Multiverse. 2025, 1-30. DOI: 10.1145/3706598.3713482.Peer-Reviewed Original ResearchMachine learningCentral design decisionsDesign decisionsData practicesModel building pipelineML pipelineInherent trade-offIterative developmentCitizen science platformScience platformSystem outputInputTrade-OffsPrivacyDiverse stakeholdersPeople's inputDecisionPipelinePublic participationParticipatory inputCausal Modeling of FMRI Time-Series for Interpretable Autism Spectrum Disorder Classification
Duan P, Dvornek N, Wang J, Staib L, Duncan J. Causal Modeling of FMRI Time-Series for Interpretable Autism Spectrum Disorder Classification. 2025, 00: 1-5. DOI: 10.1109/isbi60581.2025.10980933.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingAutism spectrum disorderState-of-the-art modelsState-of-the-artFMRI time seriesDeep learning classifierDeep learning modelsTime series informationLearning classifiersClassification accuracyNon-linear interactionsMachine learningLeft precuneusRight precuneusABIDE datasetBrain regionsLearning modelsASD populationSpectrum disorderDisorder classificationASD classificationBrain signalsASD biomarkersDevelopmental disordersCorrelation-based modelsQuantum variational autoencoder utilizing regularized mixed-state latent representations
Wang G, Warrell J, Emani P, Gerstein M. Quantum variational autoencoder utilizing regularized mixed-state latent representations. Physical Review A 2025, 111: 042416. DOI: 10.1103/physreva.111.042416.Peer-Reviewed Original ResearchQuantum modelQuantum dataVariational autoencoderLatent representationQuantum computationNear-term quantum computersQuantum hardware resourcesOptimal latent representationEfficient data compressionLow-dimensional representationQuantum componentsDensity matrixLower-dimensional spaceQuantum frameworkMixed stateFederated LearningRepresentation learningLearned representationsLatent spaceData compressionHardware resourcesMachine learningSynthetic dataData generationAutoencoderMachine Learning-Assisted Health Economics and Policy Reviews: A Comparative Assessment
Cavallaro L, Ardito V, Drummond M, Ciani O. Machine Learning-Assisted Health Economics and Policy Reviews: A Comparative Assessment. Applied Health Economics And Health Policy 2025, 23: 639-647. PMID: 40153219, PMCID: PMC12170730, DOI: 10.1007/s40258-025-00963-y.Peer-Reviewed Original ResearchThe NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering
Carlson D, Chavarriaga R, Liu Y, Lotte F, Lu B. The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineering. Journal Of Neural Engineering 2025, 22: 021002. PMID: 40073450, PMCID: PMC11948487, DOI: 10.1088/1741-2552/adbfbd.Peer-Reviewed Original ResearchMachine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis
Narimani-Javid R, Moradi M, Mahalleh M, Najafi-Vosough R, Arzhangzadeh A, Khalique O, Mojibian H, Kuno T, Mohsen A, Alam M, Shafiei S, Khansari N, Shaghaghi Z, Nozhat S, Hosseini K, Hosseini S. Machine learning and computational fluid dynamics derived FFRCT demonstrate comparable diagnostic performance in patients with coronary artery disease; A Systematic Review and Meta-Analysis. Journal Of Cardiovascular Computed Tomography 2025, 19: 232-246. PMID: 39988511, DOI: 10.1016/j.jcct.2025.02.004.Peer-Reviewed Original ResearchArea under the curveDiagnostic odds ratioDiagnostic performanceComputed tomography-derived fractional flow reserveDiagnostic performance of FFRCTHemodynamically significant coronary artery stenosisSignificant coronary artery stenosisMeta-analysisPer-patient levelReceiver operating characteristic curveCoronary artery diseaseCoronary artery stenosisPer-vessel levelFractional flow reserveNoninvasive diagnostic techniquesMachine learningInvasive FFRPooled specificityArtery diseaseArtery stenosisFlow reserveOdds ratioCochrane LibraryFFRCTPatients
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