2024
Augmenting biomedical named entity recognition with general-domain resources
Yin Y, Kim H, Xiao X, Wei C, Kang J, Lu Z, Xu H, Fang M, Chen Q. Augmenting biomedical named entity recognition with general-domain resources. Journal Of Biomedical Informatics 2024, 159: 104731. PMID: 39368529, DOI: 10.1016/j.jbi.2024.104731.Peer-Reviewed Original ResearchBioNER datasetsMulti-task learningNER datasetsEntity typesBiomedical datasetsBaseline modelGeneral domain datasetsBiomedical language modelNeural network-basedYield performance improvementsBioNER modelsEntity recognitionBiomedical corporaHuman annotatorsLabel ambiguityLanguage modelTransfer learningF1 scoreBioNERHuman effortNetwork-basedBiomedical resourcesPerformance improvementDatasetSuperior performanceAscle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study
Yang R, Zeng Q, You K, Qiao Y, Huang L, Hsieh C, Rosand B, Goldwasser J, Dave A, Keenan T, Ke Y, Hong C, Liu N, Chew E, Radev D, Lu Z, Xu H, Chen Q, Li I. Ascle—A Python Natural Language Processing Toolkit for Medical Text Generation: Development and Evaluation Study. Journal Of Medical Internet Research 2024, 26: e60601. PMID: 39361955, PMCID: PMC11487205, DOI: 10.2196/60601.Peer-Reviewed Original ResearchConceptsNatural language processingNatural language processing toolkitQuestion-answering taskLanguage modelText generationText processingDomain-specific language modelsNatural language processing functionsMinimal programming expertiseText generation tasksMedical knowledge graphMachine translation tasksROUGE-L scoreDomain-specific challengesAll-in-one solutionROUGE-LText summarizationBLEU scoreKnowledge graphMachine translationUnstructured textQuestion-answeringHugging FaceProcessing toolkitLanguage processingOutpatient reception via collaboration between nurses and a large language model: a randomized controlled trial
Wan P, Huang Z, Tang W, Nie Y, Pei D, Deng S, Chen J, Zhou Y, Duan H, Chen Q, Long E. Outpatient reception via collaboration between nurses and a large language model: a randomized controlled trial. Nature Medicine 2024, 30: 2878-2885. PMID: 39009780, DOI: 10.1038/s41591-024-03148-7.Peer-Reviewed Original ResearchRandomized controlled trialsNurse-led sessionsPrimary care concernsSingle-center randomized controlled trialCollaborative modelHealthcare experiencesCare concernsPatient queriesMedical careImprove communicationReducing negative emotionsNursesHospital workflowSecondary outcomesMedical CenterLanguage modelSatisfaction feedbackReal-world deploymentProportion of queriesNegative emotionsAudio corpusHuman effortCommunication systemsPatientsCareAn Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen Age-Related Eye Disease Study Report Number 42
Agrón E, Domalpally A, Chen Q, Lu Z, Chew E, Keenan T, Groups A. An Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen Age-Related Eye Disease Study Report Number 42. Ophthalmology 2024, 131: 1164-1174. PMID: 38657840, PMCID: PMC11416341, DOI: 10.1016/j.ophtha.2024.04.011.Peer-Reviewed Original ResearchAge-Related Eye Disease StudyProgression to late AMDReticular pseudodrusenLate AMDFive-year ratesProgression rateAge-related macular degenerationSeverity ScaleEye Disease StudyClinical trial cohortIncrease prognostic accuracyPost hoc analysisMacular degenerationAREDS2Prognostic accuracyTrial cohortRisk featuresHoc analysisRisk categorizationPseudodrusenAge-relatedBaselineDisease StudyRiskExternal validationPubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge
Wei C, Allot A, Lai P, Leaman R, Tian S, Luo L, Jin Q, Wang Z, Chen Q, Lu Z. PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge. Nucleic Acids Research 2024, 52: w540-w546. PMID: 38572754, PMCID: PMC11223843, DOI: 10.1093/nar/gkae235.Peer-Reviewed Original ResearchState-of-the-art AI techniquesState-of-the-artComplex information needsAdvanced search capabilitiesPairs queriesEntity relationsRetrieval qualitySearch capabilityAI techniquesLiterature resourcesPubTatorInformation needsPubMed abstractsBiomedical literatureOnline interfaceLarge-scale analysisGenetic variantsBiomedical knowledgeAPIScientific discoveryComprehensive setChatGPTQueryVerifiabilityRetrievalDetection of reticular pseudodrusen on optical coherence tomography images
Elsawy A, Keenan T, Agron E, Chen Q, Chew E, Lu Z. Detection of reticular pseudodrusen on optical coherence tomography images. Progress In Biomedical Optics And Imaging 2024, 12926: 1292632-1292632-5. DOI: 10.1117/12.3007014.Peer-Reviewed Original ResearchAge-related macular degenerationSD-OCT scansAge-Related Eye Disease Study 2Detect reticular pseudodrusenReticular pseudodrusenSD-OCTFundus autofluorescenceVolumetric spectral-domain optical coherence tomographySpectral-domain optical coherence tomographySubretinal drusenoid depositsOptical coherence tomography imagesPredictors of progressionOptical coherence tomographyReceiver characteristic operating curvesDrusenoid depositsMacular degenerationOCT studiesCoherence tomographyDisease featuresTomography imagesOperating curvePseudodrusenAge-relatedClassification networkMulti-taskingAdvancing entity recognition in biomedicine via instruction tuning of large language models
Keloth V, Hu Y, Xie Q, Peng X, Wang Y, Zheng A, Selek M, Raja K, Wei C, Jin Q, Lu Z, Chen Q, Xu H. Advancing entity recognition in biomedicine via instruction tuning of large language models. Bioinformatics 2024, 40: btae163. PMID: 38514400, PMCID: PMC11001490, DOI: 10.1093/bioinformatics/btae163.Peer-Reviewed Original ResearchNamed Entity RecognitionSequence labeling taskNatural language processingBiomedical NER datasetsLanguage modelNER datasetsEntity recognitionLabeling taskText generationField of natural language processingBiomedical NERFew-shot learning capabilityReasoning tasksMulti-domain scenariosDomain-specific modelsEnd-to-endMinimal fine-tuningSOTA performanceF1 scoreHealthcare applicationsBiomedical entitiesBiomedical domainLanguage processingMulti-taskingPubMedBERT modelOphthalmic care may not align with patient need: An analysis on state-wide patient needs and provider density between 2008 and 2022
Gilson A, Chen Q, Adelman R. Ophthalmic care may not align with patient need: An analysis on state-wide patient needs and provider density between 2008 and 2022. International Journal Of Medical Informatics 2024, 185: 105411. PMID: 38492409, PMCID: PMC11047060, DOI: 10.1016/j.ijmedinf.2024.105411.Peer-Reviewed Original ResearchProvider densityPatient needsDensity of ophthalmologistsOphthalmological careHealthcare availabilityResources patientsPractice locationOphthalmologic termsPatient interestOphthalmic carePatient informationImplementation strategiesPatient's desireCareRetinal specialistsEducational backgroundOphthalmologistsPatientsOphthalmologyTrends dataNeedsHealthcareGoogle Trends dataDemographic elementsProvidersGeneGPT: augmenting large language models with domain tools for improved access to biomedical information
Jin Q, Yang Y, Chen Q, Lu Z. GeneGPT: augmenting large language models with domain tools for improved access to biomedical information. Bioinformatics 2024, 40: btae075. PMID: 38341654, PMCID: PMC10904143, DOI: 10.1093/bioinformatics/btae075.Peer-Reviewed Original ResearchAPI callsWeb APIsLanguage modelState-of-the-art performanceMulti-hop questionsState-of-the-artDomain-specific toolsDecoding algorithmNational Center for Biotechnology InformationGPT-3Biomedical informationDatabase utilizationExperimental resultsAPITaskDomain toolsLearningChatGPTSpecialized knowledgeInformationLanguageGenomic questionsAlgorithmDatasetBiotechnology InformationImproving large language models for clinical named entity recognition via prompt engineering
Hu Y, Chen Q, Du J, Peng X, Keloth V, Zuo X, Zhou Y, Li Z, Jiang X, Lu Z, Roberts K, Xu H. Improving large language models for clinical named entity recognition via prompt engineering. Journal Of The American Medical Informatics Association 2024, 31: 1812-1820. PMID: 38281112, PMCID: PMC11339492, DOI: 10.1093/jamia/ocad259.Peer-Reviewed Original ResearchClinical NER tasksNER taskTask-specific promptsEntity recognitionLanguage modelTraining samplesState-of-the-art modelsFew-shot learningState-of-the-artMinimal training dataTask-specific knowledgeF1-socreAnnotated samplesConcept extractionModel performanceAnnotated datasetsTraining dataF1 scoreTask descriptionFormat specificationsComplex clinical dataOptimal performanceTaskEvaluation schemaGPT model
2023
A deep network DeepOpacityNet for detection of cataracts from color fundus photographs
Elsawy A, Keenan T, Chen Q, Thavikulwat A, Bhandari S, Quek T, Goh J, Tham Y, Cheng C, Chew E, Lu Z. A deep network DeepOpacityNet for detection of cataracts from color fundus photographs. Communications Medicine 2023, 3: 184. PMID: 38104223, PMCID: PMC10725427, DOI: 10.1038/s43856-023-00410-w.Peer-Reviewed Original ResearchColor fundus photographyAnterior segment photographsSlit-lamp examinationEye Disease StudyPosterior subcapsular cataractColor fundus photographsAREDS2 participantsCataract presenceSingapore EpidemiologyDetection of cataractOphthalmology clinicFundus photographyFundus photographsSubcapsular cataractCenter gradingCataractOphthalmologistsDisease StudyBlood vesselsNuclear cataractPerson evaluationAREDS2ClinicEpidemiologyDiagnosisOpportunities and challenges for ChatGPT and large language models in biomedicine and health
Tian S, Jin Q, Yeganova L, Lai P, Zhu Q, Chen X, Yang Y, Chen Q, Kim W, Comeau D, Islamaj R, Kapoor A, Gao X, Lu Z. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Briefings In Bioinformatics 2023, 25: bbad493. PMID: 38168838, PMCID: PMC10762511, DOI: 10.1093/bib/bbad493.Peer-Reviewed Original ResearchConceptsLarge language modelsLanguage modelSensitive patient dataBiomedical information retrievalText generation tasksInformation retrievalPrivacy concernsDomain expertsInformation extractionText summarizationBiomedical domainArt methodsDiverse applicationsPrevious stateBiomedical researchersGeneration taskPatient dataSuch methodsTaskDistinct complexityGeneration capabilityExtensive literature surveySummarizationRecent rapid progressChallengesMedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval
Jin Q, Kim W, Chen Q, Comeau D, Yeganova L, Wilbur W, Lu Z. MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval. Bioinformatics 2023, 39: btad651. PMID: 37930897, PMCID: PMC10627406, DOI: 10.1093/bioinformatics/btad651.Peer-Reviewed Original ResearchConceptsInformation retrievalIR tasksUser click logsSemantic information retrievalBiomedical information retrievalBiomedical knowledge acquisitionPre-trained TransformerClinical decision supportClick logsSearch logsContrastive learningLexical matchingArt performanceIR systemsSemantic retrievalBiomedical articlesDecision supportSentence representationModel encoderKnowledge acquisitionLarge modelsSemantic evaluationRetrievalTransformer modelUnprecedented scaleBioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets
Lai P, Wei C, Luo L, Chen Q, Lu Z. BioREx: Improving biomedical relation extraction by leveraging heterogeneous datasets. Journal Of Biomedical Informatics 2023, 146: 104487. PMID: 37673376, DOI: 10.1016/j.jbi.2023.104487.Peer-Reviewed Original ResearchBiomedical relation extractionRelation extractionRE tasksNatural language processing researchData-centric approachKnowledge graph constructionMulti-task learningLanguage processing researchIndividual datasetsLiterature-based discoveryChemical-induced disease relationsDataset annotationDomain knowledgeTransfer learningTraining dataHeterogeneous datasetsArt methodsNovel frameworkGraph constructionFree textData heterogeneityLarge datasetsBiomedical conceptsProcessing researchDatasetFrom function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence
Long E, Wan P, Chen Q, Lu Z, Choi J. From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence. Cell Genomics 2023, 3: 100320. PMID: 37388909, PMCID: PMC10300605, DOI: 10.1016/j.xgen.2023.100320.Peer-Reviewed Original ResearchDisease-associated lociWide association studyNovel biological insightsFunctional genomicsPost-GWASGWAS findingsGWA findingsBiological insightsMolecular mechanismsAssociation studiesHuman diseasesGenetic associationFunctional datasetsLociDisease etiologyGenetic susceptibilityGenomicsConsiderable fractionAIONER: all-in-one scheme-based biomedical named entity recognition using deep learning
Luo L, Wei C, Lai P, Leaman R, Chen Q, Lu Z. AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning. Bioinformatics 2023, 39: btad310. PMID: 37171899, PMCID: PMC10212279, DOI: 10.1093/bioinformatics/btad310.Peer-Reviewed Original ResearchConceptsDeep learningEntity recognitionTraining dataEntity typesLabeling training dataNatural language textText mining tasksSignificant domain expertiseMulti-task learningMining tasksInformation extractionBioNER taskDomain expertiseBiomedical entitiesIndependent tasksSource codeBenchmark tasksLanguage textBiomedical textArt approachesAccurate annotationExternal dataData scarcityTaskLearningDeep-GA-Net for Accurate and Explainable Detection of Geographic Atrophy on OCT Scans
Elsawy A, Keenan T, Chen Q, Shi X, Thavikulwat A, Bhandari S, Chew E, Lu Z. Deep-GA-Net for Accurate and Explainable Detection of Geographic Atrophy on OCT Scans. Ophthalmology Science 2023, 3: 100311. PMID: 37304045, PMCID: PMC10251072, DOI: 10.1016/j.xops.2023.100311.Peer-Reviewed Original ResearchLarge language models and the retina: a review of current applications and future directions
Gilson A, Chen Q, Singer M, Xu H, Adelman R. Large language models and the retina: a review of current applications and future directions. Journal Of Retina-Vitreous 2023, 32: 225. DOI: 10.37845/ret.vit.2023.32.38.Peer-Reviewed Original Research
2022
Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning
Lee J, Wanyan T, Chen Q, Keenan T, Glicksberg B, Chew E, Lu Z, Wang F, Peng Y. Predicting Age-related Macular Degeneration Progression with Longitudinal Fundus Images Using Deep Learning. Lecture Notes In Computer Science 2022, 13583: 11-20. PMID: 36656604, PMCID: PMC9842432, DOI: 10.1007/978-3-031-21014-3_2.Peer-Reviewed Original ResearchLate age-related macular degenerationAge-related macular degenerationColor fundus photographsEye Disease StudyRisk prediction modelMacular degeneration progressionTriaging patientsFundus photographsPatient riskAMD cohortMacular degenerationPatient historyDegeneration progressionDisease StudyProgressionRiskSubsequent time intervalsPersonalized medicineAgeFundus imagesPatientsCohortDegenerationBaselineReticular Pseudodrusen Status, ARMS2/HTRA1 Genotype, and Geographic Atrophy Enlargement Age-Related Eye Disease Study 2 Report 32
Agrón E, Domalpally A, Cukras C, Clemons T, Chen Q, Swaroop A, Lu Z, Chew E, Keenan T, Groups A. Reticular Pseudodrusen Status, ARMS2/HTRA1 Genotype, and Geographic Atrophy Enlargement Age-Related Eye Disease Study 2 Report 32. Ophthalmology 2022, 130: 488-500. PMID: 36481221, PMCID: PMC10121754, DOI: 10.1016/j.ophtha.2022.11.026.Peer-Reviewed Original ResearchConceptsARMS2 genotypeCentral maculaAtrophy enlargementRisk allelesHTRA1 risk allelesIndependent risk factorCentral involvementEye Disease StudyFundus autofluorescence imagesGeographic atrophy areaEnlargement rateMixed model regressionAtrophy areaCommercial disclosureRisk factorsFundus photographsAnnual visitsFaster progressionGA progressionGA incidenceDisease StudySimilar findingsAutofluorescence imagesPotential mediationFaster enlargement