2025
Validity of Diagnostic Codes and Laboratory Tests to Identify Cholangiocarcinoma and Its Subtypes
Ferrante N, Hubbard R, Weinfurtner K, Mezina A, Newcomb C, Furth E, Bhattacharya D, Njei B, Taddei T, Singal A, Hoteit M, Park L, Kaplan D, Re V. Validity of Diagnostic Codes and Laboratory Tests to Identify Cholangiocarcinoma and Its Subtypes. Pharmacoepidemiology And Drug Safety 2025, 34: e70154. PMID: 40328444, PMCID: PMC12055315, DOI: 10.1002/pds.70154.Peer-Reviewed Original ResearchConceptsPositive predictive valueVeterans Health AdministrationExtrahepatic cholangiocarcinomaValidity of diagnostic codesInternational Classification of Diseases for OncologyUS Veterans Health AdministrationConfidence intervalsPharmacoepidemiological studiesICD-O-3Days of diagnosisVA dataHealth AdministrationIntrahepatic cholangiocarcinomaDiagnostic codesHistology codesCholangiocarcinomaUnique patientsInclusion criteriaCholangiocarcinoma subtypesTopography codesPredictive valuePatientsEvaluate medicationsSubtypesEvaluate determinantsEnsemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Shankar S, Coppi A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD. Journal Of The American College Of Cardiology 2025, 85: 1302-1313. PMID: 40139886, DOI: 10.1016/j.jacc.2025.01.030.Peer-Reviewed Original ResearchConceptsStructural heart diseaseYale-New Haven HospitalTransthoracic echocardiogramRisk stratificationHeart failureLeft-sided valvular diseaseSevere left ventricular hypertrophyLeft ventricular ejection fractionReceiver-operating characteristic curveVentricular ejection fractionLeft ventricular hypertrophyHeart disease screeningELSA-BrasilEnsemble deep learning algorithmRisk of deathConvolutional neural network modelEjection fractionEnsemble deep learning approachVentricular hypertrophyDeep learning algorithmsNew Haven HospitalDeep learning approachValvular diseaseNeural network modelClinical cohortPatellar tilt calculation utilizing artificial intelligence on CT knee imaging
Sieberer J, Rancu A, Park N, Desroches S, Manafzadeh A, Tommasini S, Wiznia D, Fulkerson J. Patellar tilt calculation utilizing artificial intelligence on CT knee imaging. The Knee 2025, 54: 217-221. PMID: 40086415, DOI: 10.1016/j.knee.2025.02.019.Peer-Reviewed Original ResearchOptimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants
Yuan M, Goovaerts S, Lee M, Devine J, Richmond S, Walsh S, Shriver M, Shaffer J, Marazita M, Peeters H, Weinberg S, Claes P. Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants. Briefings In Bioinformatics 2025, 26: bbaf090. PMID: 40062617, PMCID: PMC11891655, DOI: 10.1093/bib/bbaf090.Peer-Reviewed Original ResearchConceptsRare variant association studiesGenome-wide association studiesComplex morphological traitsGenomic lociSNP heritabilityAssociation studiesRare variantsPhenotypic variationMorphological traitsAxes of phenotypic variationContext of genome-wide association studiesVariant association studiesIndividuals of European ancestryGene-based testsLinkage disequilibrium score regressionRare genetic variantsGenomic relatednessOptimal phenotypeUnrelated individualsGenetic variantsRelevant traitsEuropean ancestryScore regressionPhenotype distributionFamily dataEmpowering genome-wide association studies via a visualizable test based on the regional association score
Jiang Y, Zhang H. Empowering genome-wide association studies via a visualizable test based on the regional association score. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2419721122. PMID: 39999171, PMCID: PMC11892588, DOI: 10.1073/pnas.2419721122.Peer-Reviewed Original ResearchBatch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns
Millard N, Chen J, Palshikar M, Pelka K, Spurrell M, Price C, He J, Hacohen N, Raychaudhuri S, Korsunsky I. Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns. Genome Biology 2025, 26: 36. PMID: 40001084, PMCID: PMC11863647, DOI: 10.1186/s13059-025-03479-9.Peer-Reviewed Original ResearchConceptsBatch effectsVisualization of gene expression patternsSpatial gene patternsGene expression analysis of cellsGene expression patternsGene expression analysisGene expression levelsGene colocalizationAnalysis of cellsGene patternsTranscriptome analysisLigand-receptor interactionsExpression patternsSpatial transcriptomicsSpatial transcriptomic analysisExpression levelsGenesMultiple samplesSpatial patternsTranscriptomeColocalizationAnatomical contextPatternsCount dataThe development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas
Jester N, Singh M, Lorr S, Tommasini S, Wiznia D, Buono F. The development of an artificial intelligence auto-segmentation tool for 3D volumetric analysis of vestibular schwannomas. Scientific Reports 2025, 15: 5918. PMID: 39966622, PMCID: PMC11836447, DOI: 10.1038/s41598-025-88589-x.Peer-Reviewed Original ResearchConceptsGround-truth datasetDice scoreVestibular schwannomaImage processing accuracyVolumetric analysisML-based algorithmsMeasuring tumor sizeMean dice scoreAuto-segmentation toolAccurate AIAI modelsTumor sizeTumor modelVS tumorsTumor growthTesting stageAI-LTumorImage processing softwareClinical practicePatient recruitmentProcessing softwareSchwannomaDatasetManual segmentationAcute Myeloid Leukemia: 2025 Update on Diagnosis, Risk‐Stratification, and Management
Shimony S, Stahl M, Stone R. Acute Myeloid Leukemia: 2025 Update on Diagnosis, Risk‐Stratification, and Management. American Journal Of Hematology 2025, 100: 860-891. PMID: 39936576, PMCID: PMC11966364, DOI: 10.1002/ajh.27625.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsDisease ManagementHumansLeukemia, Myeloid, AcuteNeoplasm, ResidualPrognosisRisk AssessmentConceptsAcute myeloid leukemiaTherapeutic management of acute myeloid leukemiaManagement of acute myeloid leukemiaEuropean Leukemia NetworkStem cell cancerTherapeutic decision-makingImmature leukemia cellsExtra-medullary tissuesMRD findingsPrognostic factorsCell cancerTherapeutic algorithmRisk classification algorithmApproved therapiesMyeloid leukemiaResponse assessmentRisk stratificationBone marrowTherapeutic managementMonitoring of disease statusPathophysiological understandingDisease characteristicsMolecular findingsTherapeutic approachesDisease statusEvaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies
Jing N, Lu Y, Tong J, Weaver J, Ryan P, Xu H, Chen Y. Evaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies. Journal Of Biomedical Informatics 2025, 163: 104787. PMID: 39904407, DOI: 10.1016/j.jbi.2025.104787.Peer-Reviewed Original ResearchConceptsType I errorIntegrated likelihood estimatorsElectronic health recordsUse-case analysisLikelihood estimationLow prevalence outcomesUse-casesBias reductionNaive methodEffect sizeSynthetic dataPhenotyping algorithmsEstimation biasReal-world scenariosStatistical inferenceSimulation studyAssociation effect sizesAccurate prior informationBinary outcomesPoint estimatesAssociation estimatesStatistical powerHealth recordsKnowledge-guidedOutcome prevalencePerformance of algorithms using wrist temperature for retrospective ovulation day estimate and next menses start day prediction: a prospective cohort study
Wang Y, Park J, Zhang C, Jukic A, Baird D, Coull B, Hauser R, Mahalingaiah S, Zhang S, Curry C. Performance of algorithms using wrist temperature for retrospective ovulation day estimate and next menses start day prediction: a prospective cohort study. Human Reproduction 2025, 40: 469-478. PMID: 39881571, PMCID: PMC11879225, DOI: 10.1093/humrep/deaf005.Peer-Reviewed Original ResearchConceptsNext mensesDay of ovulationProspective cohort studyMenstrual cycleBasal body temperatureLH testCohort studyCycle lengthWomen's Health StudyROLE OF CHANCEDaily basal body temperatureAssociated with ovulationFUNDING/COMPETING INTEREST(SWIDER IMPLICATIONSMensesWrist temperatureMenstruating femalesHormonal changesOvulation dayHierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling
Li J, Zhang Q, Ma S, Fang K, Xu Y. Hierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling. Statistics In Medicine 2025, 44: e10330. PMID: 39865593, DOI: 10.1002/sim.10330.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputer SimulationGene-Environment InteractionHumansLung NeoplasmsModels, StatisticalConceptsHierarchical multi-label classificationMulti-label classificationGene-environment interaction analysisGene-environmentEfficient expectation-maximizationGene-environment interactionsSemi-supervised scenariosCancer Genome AtlasUnlabeled dataInteraction analysisExpectation-maximizationGenome AtlasSuperior performanceHierarchical responseDisease outcomeClassificationPenalized estimatorsPractice settingsDisease modelsBiomedical studiesAnalysis literatureE effectsBiomedRAG: A retrieval augmented large language model for biomedicine
Li M, Kilicoglu H, Xu H, Zhang R. BiomedRAG: A retrieval augmented large language model for biomedicine. Journal Of Biomedical Informatics 2025, 162: 104769. PMID: 39814274, PMCID: PMC11837810, DOI: 10.1016/j.jbi.2024.104769.Peer-Reviewed Original ResearchUtility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder
Davis C, Jinwala Z, Hatoum A, Toikumo S, Agrawal A, Rentsch C, Edenberg H, Baurley J, Hartwell E, Crist R, Gray J, Justice A, Gelernter J, Kember R, Kranzler H, Muralidhar S, Moser J, Deen J, Tsao P, Gaziano J, Hauser E, Kilbourne A, Matheny M, Oslin D, Churby L, Whitbourne S, Brewer J, Shayan S, Selva L, Pyarajan S, Cho K, DuVall S, Brophy M, Stephens B, Connor T, Argyres D, Assimes T, Hung A, Kranzler H, Aguayo S, Ahuja S, Alexander K, Androulakis X, Balasubramanian P, Ballas Z, Beckham J, Bhushan S, Boyko E, Cohen D, Dellitalia L, Faulk L, Fayad J, Fujii D, Gappy S, Gesek F, Greco J, Godschalk M, Gress T, Gupta S, Gutierrez S, Harley J, Hamner M, Hurley R, Iruvanti P, Jacono F, Jhala D, Kinlay S, Landry M, Liang P, Liangpunsakul S, Lichy J, Mahan C, Marrache R, Mastorides S, Mattocks K, Meyer P, Moorman J, Morgan T, Murdoch M, Norton J, Okusaga O, Oursler K, Poon S, Rauchman M, Servatius R, Sharma S, Smith R, Sriram P, Strollo P, Tandon N, Villareal G, Walsh J, Wells J, Whittle J, Whooley M, Wilson P, Xu J, Yeh S, Bast E, Dryden G, Hogan D, Joshi S, Lo T, Morales P, Naik E, Ong M, Petrakis I, Rai A, Yen A. Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder. JAMA Network Open 2025, 8: e2453913. PMID: 39786773, PMCID: PMC11718552, DOI: 10.1001/jamanetworkopen.2024.53913.Peer-Reviewed Original ResearchConceptsOpioid use disorder riskElectronic health record dataHealth record dataInternational Classification of DiseasesOpioid use disorderClassification of DiseasesGenetic variantsInternational ClassificationGenetic riskRecord dataRisk of opioid use disorderMillion Veteran ProgramOpioid use disorder diagnosisUse disorderCase-control studyVeteran ProgramMain OutcomesDiagnostic codesClinical careOpioid exposurePharmacy recordsLogistic regressionRisk allelesNagelkerke R2Clinically useful modelMachine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor
Yu G, Wang X, Luo Y, Li G, Ding R, Shi R, Huo X, Yang Y. Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor. Journal Of Chemical Information And Modeling 2025, 65: 312-325. PMID: 39744764, DOI: 10.1021/acs.jcim.4c02120.Peer-Reviewed Original ResearchConceptsAllylic substitutionReaction outcomeDensity functional theory calculationsAllylic substitution reactionsPredictions of reaction outcomesFunctional theory calculationsOrganic synthesis fieldSubstitution reactionMachine learningCatalyst optimizationTheory calculationsReaction mechanismMolecular propertiesSynthesis fieldAtomic levelAllylationGraph matching algorithmReactionExtract essential informationAtomic featuresMainstream descriptorsMatching algorithmSubstrate combinationsAutomatic extractionDescriptorsUnsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers.
Jacokes Z, Adoremos I, Hussain A, Newman B, Pelphrey K, Van Horn J. Unsupervised Dimensionality Reduction Techniques for the Assessment of ASD Biomarkers. Biocomputing 2025, 30: 614-630. PMID: 39670400.Peer-Reviewed Original ResearchConceptsAutism spectrum disorderNeural basisDevelopmental disabilitiesCognitive traitsDiagnostic disparitiesSpectrum disorderNeuroimaging techniquesASD biomarkersSex differencesSocial functioningASD researchEffects of gene expressionNeuronal microstructureTailored interventionsBiological influencesGenetic interactionsGene expressionPhenotypic heterogeneityAutismCognitionPrincipal component analysis
2024
Real-world individual and comparative analysis of adverse event reporting for adalimumab and etanercept using public FDA adverse event reporting system data
Dou X, Dai Y, Zhu L, Lin Y, Wu Y. Real-world individual and comparative analysis of adverse event reporting for adalimumab and etanercept using public FDA adverse event reporting system data. Archives Of Dermatological Research 2024, 317: 161. PMID: 39738670, DOI: 10.1007/s00403-024-03626-5.Peer-Reviewed Original ResearchStyle mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation
Cai Z, Xin J, You C, Shi P, Dong S, Dvornek N, Zheng N, Duncan J. Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation. Medical Image Analysis 2024, 101: 103440. PMID: 39764933, DOI: 10.1016/j.media.2024.103440.Peer-Reviewed Original ResearchConceptsUnsupervised domain adaptationMedical image segmentationDomain-invariant representationsImage segmentationDomain adaptationDisentanglement learningImage translationUnsupervised domain adaptation approachState-of-the-art methodsDomain shift problemDomain-invariant learningState-of-the-artPublic cardiac datasetsDiverse constraintsAdversarial learningConsistency regularizationContrastive learningFeature spaceSemantic consistencyComprehensive experimentsDomain generalizationData diversityShift problemMedical segmentationCardiac datasetsnipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares
Mattessich M, Reyna J, Aron E, Ay F, Kilmer M, Kleinstein S, Konstorum A. nipalsMCIA: flexible multi-block dimensionality reduction in R via nonlinear iterative partial least squares. Bioinformatics 2024, 41: btaf015. PMID: 39799512, PMCID: PMC11783316, DOI: 10.1093/bioinformatics/btaf015.Peer-Reviewed Original ResearchConceptsIterative partial least squaresNonlinear iterative partial least squaresDimensionality reductionMultiple co-inertia analysisJoint dimensionality reductionSignificant speed-upUnsupervised learningSingle-cell datasetsMulti-omics dataCo-inertia analysisFeature dimensionsSpeed-upBioconductor packageSingle-cell analysisPartial least squaresLeast squaresRobust approachImplementationHTMLDatasetBioconductorAccuracy of Electronic Health Record Phenotypes to Detect Recognition of Hypertension in Pediatric Primary Care
Nugent J, Cueto V, Tong C, Sharifi M. Accuracy of Electronic Health Record Phenotypes to Detect Recognition of Hypertension in Pediatric Primary Care. Academic Pediatrics 2024, 25: 102629. PMID: 39732164, PMCID: PMC11893226, DOI: 10.1016/j.acap.2024.102629.Peer-Reviewed Original ResearchConceptsPediatric primary careIncident hypertensionHypertensive BPHypertension recognitionPrimary careRecognition of hypertensionCross-sectional study of children aged 3Diagnosis codesElectronic health record phenotypingClinician recognitionClinician decision supportGuideline-recommended careElectronic health recordsInternational Classification of DiseasesChart reviewDocumentation of hypertensionClassification of DiseasesCross-sectional studyChildren aged 3Problem list entriesWellness visitsHealth recordsEHR phenotypesInternational ClassificationICD-10Robust, fully-automated assessment of cerebral perivascular spaces and white matter lesions: a multicentre MRI longitudinal study of their evolution and association with risk of dementia and accelerated brain atrophy
Barisano G, Iv M, Choupan J, Hayden-Gephart M, Weiner M, Aisen P, Petersen R, Jack C, Jagust W, Trojanowki J, Toga A, Beckett L, Green R, Saykin A, Morris J, Shaw L, Liu E, Montine T, Thomas R, Donohue M, Walter S, Gessert D, Sather T, Jiminez G, Harvey D, Donohue M, Bernstein M, Fox N, Thompson P, Schuff N, DeCarli C, Borowski B, Gunter J, Senjem M, Vemuri P, Jones D, Kantarci K, Ward C, Koeppe R, Foster N, Reiman E, Chen K, Mathis C, Landau S, Cairns N, Householder E, Reinwald L, Lee V, Korecka M, Figurski M, Crawford K, Neu S, Foroud T, Potkin S, Shen L, Kelley F, Kim S, Nho K, Kachaturian Z, Frank R, Snyder P, Molchan S, Kaye J, Quinn J, Lind B, Carter R, Dolen S, Schneider L, Pawluczyk S, Beccera M, Teodoro L, Spann B, Brewer J, Vanderswag H, Fleisher A, Heidebrink J, Lord J, Petersen R, Mason S, Albers C, Knopman D, Johnson K, Doody R, Meyer J, Chowdhury M, Rountree S, Dang M, Stern Y, Honig L, Bell K, Ances B, Morris J, Carroll M, Leon S, Householder E, Mintun M, Schneider S, Oliver A, Marson D, Griffith R, Clark D, Geld-macher D, Brockington J, Roberson E, Grossman H, Mitsis E, deToledo-Morrell L, Shah R, Duara R, Varon D, Greig M, Roberts P, Albert M, Onyike C, D’Agostino D, Kielb S, Galvin J, Pogorelec D, Cerbone B, Michel C, Rusinek H, de Leon M, Glodzik L, De Santi S, Doraiswamy P, Petrella J, Wong T, Arnold S, Karlawish J, Wolk D, Smith C, Jicha G, Hardy P, Sinha P, Oates E, Conrad G, Lopez O, Oakley M, Simpson D, Porsteinsson A, Goldstein B, Martin K, Makino K, Ismail M, Brand C, Mulnard R, Thai G, Mc Adams Ortiz C, Womack K, Mathews D, Quiceno M, Arrastia R, King R, Weiner M, Cook K, DeVous M, Levey A, Lah J, Cellar J, Burns J, Anderson H, Swerdlow R, Apostolova L, Tingus K, Woo E, Silverman D, Lu P, Bartzokis G, Radford N, Parfitt F, Kendall T, Johnson H, Farlow M, Hake A, Matthews B, Herring S, Hunt C, van Dyck C, Carson R, MacAvoy M, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung G, Feldman H, Mudge B, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Kerwin D, Mesulam M, Lipowski K, Wu C, Johnson N, Sadowsky C, Martinez W, Villena T, Turner R, Johnson K, Reynolds B, Sperling R, Johnson K, Marshall G, Frey M, Yesavage J, Taylor J, Lane B, Rosen A, Tinklenberg J, Sabbagh M, Belden C, Jacobson S, Sirrel S, Kowall N, Killiany R, Budson A, Norbash A, Johnson P, Obisesan T, Wolday S, Allard J, Lerner A, Ogrocki P, Hudson L, Fletcher E, Carmichael O, Olichney J, DeCarli C, Kittur S, Borrie M, Lee T, Bartha R, Johnson S, Asthana S, Carlsson C, Potkin S, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Scharre D, Kataki M, Adeli A, Zimmerman E, Celmins D, Brown A, Pearlson G, Blank K, Anderson K, Santulli R, Kitzmiller T, Schwartz E, Sink K, Williamson J, Garg P, Watkins F, Ott B, Querfurth H, Tremont G, Salloway S, Malloy P, Correia S, Rosen H, Miller B, Mintzer J, Spicer K, Bachman D, Finger E, Pasternak S, Rachinsky I, Rogers J, Kertesz A, Drost D, Pomara N, Hernando R, Sarrael A, Schultz S, Ponto L, Shim H, Smith K, Relkin N, Chaing G, Raudin L, Smith A, Fargher K, Raj B. Robust, fully-automated assessment of cerebral perivascular spaces and white matter lesions: a multicentre MRI longitudinal study of their evolution and association with risk of dementia and accelerated brain atrophy. EBioMedicine 2024, 111: 105523. PMID: 39721217, PMCID: PMC11732520, DOI: 10.1016/j.ebiom.2024.105523.Peer-Reviewed Original ResearchConceptsRisk of dementiaDementia riskAccelerated brain atrophyLower risk of dementiaMeasures of cognitive functionLongitudinal studyUS National Institutes of HealthNational Institutes of HealthCombat dementiaInstitutes of HealthPerivascular spaces countAlzheimer's disease biomarkersUS National InstitutesClinical measures of cognitive functioningDementiaBrain healthMixed-effects modelsScreening toolClinical measuresConfounding factorsObservational studyCognitive declineLongitudinal trajectoriesParticipantsBrain atrophy
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