2024
Ascle—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 processing
2023
Opportunities 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 progressChallenges
2022
Reticular 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 enlargementLitCovid in 2022: an information resource for the COVID-19 literature
Chen Q, Allot A, Leaman R, Wei C, Aghaarabi E, Guerrerio J, Xu L, Lu Z. LitCovid in 2022: an information resource for the COVID-19 literature. Nucleic Acids Research 2022, 51: d1512-d1518. PMID: 36350613, PMCID: PMC9825538, DOI: 10.1093/nar/gkac1005.Peer-Reviewed Original ResearchLitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation
Chen Q, Du J, Allot A, Lu Z. LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation. IEEE/ACM Transactions On Computational Biology And Bioinformatics 2022, 19: 2584-2595. PMID: 35536809, PMCID: PMC9647722, DOI: 10.1109/tcbb.2022.3173562.Peer-Reviewed Original ResearchMulti-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations
Chen Q, Allot A, Leaman R, Islamaj R, Du J, Fang L, Wang K, Xu S, Zhang Y, Bagherzadeh P, Bergler S, Bhatnagar A, Bhavsar N, Chang Y, Lin S, Tang W, Zhang H, Tavchioski I, Pollak S, Tian S, Zhang J, Otmakhova Y, Yepes A, Dong H, Wu H, Dufour R, Labrak Y, Chatterjee N, Tandon K, Laleye F, Rakotoson L, Chersoni E, Gu J, Friedrich A, Pujari S, Chizhikova M, Sivadasan N, Vg S, Lu Z. Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations. Database 2022, 2022: baac069. PMID: 36043400, PMCID: PMC9428574, DOI: 10.1093/database/baac069.Peer-Reviewed Original ResearchReticular Pseudodrusen: The Third Macular Risk Feature for Progression to Late Age-Related Macular Degeneration Age-Related Eye Disease Study 2 Report 30
Agrón E, Domalpally A, Cukras C, Clemons T, Chen Q, Lu Z, Chew E, Keenan T, Groups A. Reticular Pseudodrusen: The Third Macular Risk Feature for Progression to Late Age-Related Macular Degeneration Age-Related Eye Disease Study 2 Report 30. Ophthalmology 2022, 129: 1107-1119. PMID: 35660417, PMCID: PMC9509418, DOI: 10.1016/j.ophtha.2022.05.021.Peer-Reviewed Original ResearchConceptsLate age-related macular degenerationAge-related macular degenerationAge-related eye disease studyNeovascular age-related macular degenerationColor fundus photographsHazard ratioReticular pseudodrusenGeographic atrophyHigh riskRisk factorsSeverity ScalePresence of RPDProportional hazards regression analysisMacular Degeneration AgeClinical trial cohortIndependent risk factorEye Disease StudyHazards regression analysisImportant risk factorFundus autofluorescence imagesAMD severity scaleTrial cohortRisk calculatorClinical trialsFundus photographsMulti-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS.
Ghahramani G, Brendel M, Lin M, Chen Q, Keenan T, Chen K, Chew E, Lu Z, Peng Y, Wang F. Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS. AMIA Annual Symposium Proceedings 2022, 2021: 506-515. PMID: 35308963.Peer-Reviewed Original ResearchConceptsAge-related macular degenerationImage featuresMulti-task learning frameworkConvolutional neural networkVision lossLate age-related macular degenerationEye Disease StudyLearning frameworkNeural networkFundus photographsPatient riskMacular degenerationStandard featuresSevere formComplex featuresSurvival analysisCurrent visitLongitudinal dataDisease StudyHistorical dataRapid paceFeaturesNetworkAREDSPatientsDetecting visually significant cataract using retinal photograph-based deep learning
Tham Y, Goh J, Anees A, Lei X, Rim T, Chee M, Wang Y, Jonas J, Thakur S, Teo Z, Cheung N, Hamzah H, Tan G, Husain R, Sabanayagam C, Wang J, Chen Q, Lu Z, Keenan T, Chew E, Tan A, Mitchell P, Goh R, Xu X, Liu Y, Wong T, Cheng C. Detecting visually significant cataract using retinal photograph-based deep learning. Nature Aging 2022, 2: 264-271. PMID: 37118370, PMCID: PMC10154193, DOI: 10.1038/s43587-022-00171-6.Peer-Reviewed Original ResearchDeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity
Keenan T, Chen Q, Agrón E, Tham Y, Goh J, Lei X, Ng Y, Liu Y, Xu X, Cheng C, Bikbov M, Jonas J, Bhandari S, Broadhead G, Colyer M, Corsini J, Cousineau-Krieger C, Gensheimer W, Grasic D, Lamba T, Magone M, Maiberger M, Oshinsky A, Purt B, Shin S, Thavikulwat A, Lu Z, Chew E, Group A, Ajilore P, Akman A, Azar N, Azar W, Chan B, Cox V, Dave A, Dhanjal R, Donovan M, Farrell M, Finkel F, Goblirsch T, Ha W, Hill C, Kumar A, Kent K, Lee A, Patel P, Peprah D, Piliponis E, Selzer E, Swaby B, Tenney S, Zeleny A. DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity. Ophthalmology 2022, 129: 571-584. PMID: 34990643, PMCID: PMC9038670, DOI: 10.1016/j.ophtha.2021.12.017.Peer-Reviewed Original ResearchConceptsAge-related cataractSingapore Malay Eye StudyAnterior segment photographsCortical lens opacitiesPosterior subcapsular cataractCommon typeSlit-lamp photographsLeast common typeMedical studentsEye StudyNuclear sclerosisSubcapsular cataractLens opacitiesCataract typesRetroillumination photographsCataract assessmentOphthalmologistsCataract severityCataractExternal validationDiagnosisSeveritySclerosisStudy dataset
2021
Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing
Chen Q, Leaman R, Allot A, Luo L, Wei C, Yan S, Lu Z. Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing. Annual Review Of Biomedical Data Science 2021, 4: 1-27. PMID: 34465169, DOI: 10.1146/annurev-biodatasci-021821-061045.Peer-Reviewed Original ResearchConceptsNatural language processingArtificial intelligenceLanguage processingInformation needsLiterature-based discoveryInformation retrievalEntity recognitionMisinformation detectionInformation overloadNLP studiesNLP tasksEmotion analysisTopic modelingCOVID-19 pandemicIntelligenceAdditional tasksHuman languagePublic health measuresTaskHealth measuresProcessingSerious health effectsHealth effectsRetrievalDatasetLitSuggest: a web-based system for literature recommendation and curation using machine learning
Allot A, Lee K, Chen Q, Luo L, Lu Z. LitSuggest: a web-based system for literature recommendation and curation using machine learning. Nucleic Acids Research 2021, 49: w352-w358. PMID: 33950204, PMCID: PMC8262723, DOI: 10.1093/nar/gkab326.Peer-Reviewed Original ResearchConceptsNatural language processingWeb-based systemQuery methodSearch systemSearch queriesMachine learningWeb serverCuration servicesAdvanced machineUser projectsLanguage processingClassification resultsTraining corpusSingle interfaceUsersBiomedical researchersCollaborative analysisHigh accuracyLiterature recommendationsPubMed articlesMachineCurationComputational methodsSpecialized knowledgeKeywordsMultimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration
Chen Q, Keenan T, Allot A, Peng Y, Agrón E, Domalpally A, Klaver C, Luttikhuizen D, Colyer M, Cukras C, Wiley H, Magone M, Cousineau-Krieger C, Wong W, Zhu Y, Chew E, Lu Z, Group F. Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration. Journal Of The American Medical Informatics Association 2021, 28: 1135-1148. PMID: 33792724, PMCID: PMC8200273, DOI: 10.1093/jamia/ocaa302.Peer-Reviewed Original ResearchConceptsColor fundus photographyAge-related macular degenerationFundus autofluorescenceReticular pseudodrusenMacular degenerationStandard color fundus photographyReceiver-operating characteristic curveAdvanced imaging modalitiesExternal validationRetinal specialistsAMD featuresFundus photographyGeographic atrophyPigmentary abnormalitiesAMD diagnosisImaging modalitiesCharacteristic curvePseudodrusenDegeneration
2020
LitCovid: an open database of COVID-19 literature
Chen Q, Allot A, Lu Z. LitCovid: an open database of COVID-19 literature. Nucleic Acids Research 2020, 49: d1534-d1540. PMID: 33166392, PMCID: PMC7778958, DOI: 10.1093/nar/gkaa952.Peer-Reviewed Original ResearchConceptsSerious information overloadCuration workflowData miningInformation overloadCollected articlesInformation needsOpen databaseManual curationNews articlesCOVID-19 literatureLiterature resourcesRapid growthUsersCOVID-19 researchMiningWorkflowAlgorithmCurationDate scientific informationDatabaseInformationGeneral publicResourcesAccessTextDeep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2
Keenan T, Chen Q, Peng Y, Domalpally A, Agrón E, Hwang C, Thavikulwat A, Lee D, Li D, Wong W, Lu Z, Chew E. Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2. Ophthalmology 2020, 127: 1674-1687. PMID: 32447042, PMCID: PMC11079794, DOI: 10.1016/j.ophtha.2020.05.036.Peer-Reviewed Original ResearchDeep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
Chen Q, Du J, Kim S, Wilbur W, Lu Z. Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records. BMC Medical Informatics And Decision Making 2020, 20: 73. PMID: 32349758, PMCID: PMC7191680, DOI: 10.1186/s12911-020-1044-0.Peer-Reviewed Original ResearchConceptsEnd deep learning modelEncoder networkDeep learning modelsSentence embeddingsBiomedical corporaLearning modelRandom forestTraditional machineText mining applicationsDeep learning approachSimilar sentencesMachine learning modelsHigh performanceMining applicationsRelated datasetsClinical notesLearning approachSentence semanticsPubMed abstractsChallenge taskEnsembled modelBest submissionSentence pairsNetworkTest setBioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale
Chen Q, Lee K, Yan S, Kim S, Wei C, Lu Z. BioConceptVec: Creating and evaluating literature-based biomedical concept embeddings on a large scale. PLOS Computational Biology 2020, 16: e1007617. PMID: 32324731, PMCID: PMC7237030, DOI: 10.1371/journal.pcbi.1007617.Peer-Reviewed Original ResearchConceptsConcept embeddingsNER toolsLearning modelBiomedical text mining applicationsAdvanced deep learning modelsDifferent machine learning modelsEvaluation resultsText mining applicationsDeep learning modelsSemantics of conceptsMachine learning modelsLiterature-based discoveryConcept recognitionDifferent machineProtein-protein interaction predictionPubMed abstractsRecognition toolsMassive numberVector representationBiomedical conceptsLarge marginExtrinsic evaluationBiomedical literatureIntrinsic evaluationSemantic relatednessKeep up with the latest coronavirus research
Chen Q, Allot A, Lu Z. Keep up with the latest coronavirus research. Nature 2020, 579: 193-193. PMID: 32157233, DOI: 10.1038/d41586-020-00694-1.Peer-Reviewed Original Research
2019
A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs
Keenan T, Dharssi S, Peng Y, Chen Q, Agrón E, Wong W, Lu Z, Chew E. A Deep Learning Approach for Automated Detection of Geographic Atrophy from Color Fundus Photographs. Ophthalmology 2019, 126: 1533-1540. PMID: 31358385, PMCID: PMC6810830, DOI: 10.1016/j.ophtha.2019.06.005.Peer-Reviewed Original ResearchOverview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine
Doğan R, Kim S, Chatr-aryamontri A, Wei C, Comeau D, Antunes R, Matos S, Chen Q, Elangovan A, Panyam N, Verspoor K, Liu H, Wang Y, Liu Z, Altınel B, Hüsünbeyi Z, Özgür A, Fergadis A, Wang C, Dai H, Tran T, Kavuluru R, Luo L, Steppi A, Zhang J, Qu J, Lu Z. Overview of the BioCreative VI Precision Medicine Track: mining protein interactions and mutations for precision medicine. Database 2019, 2019: bay147. PMID: 30689846, PMCID: PMC6348314, DOI: 10.1093/database/bay147.Peer-Reviewed Original ResearchConceptsRelation extraction taskDocument triage taskBest F-scoreExtraction taskTriage taskKnowledge basesF-scorePubMed documentsArt deep learning methodsText-mining research communityLarge knowledge basesDeep learning methodsText mining systemText mining modelText mining toolsBest average precisionData setsLarge-scale corpusHuman annotationsElectronic health recordsSystem developersBetter recallText miningAverage precisionLearning methods