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, 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 processingAugmenting 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, 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 performanceOutpatient 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, 1-8. 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 systemsPatientsCareAdvancing 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 modelGeneGPT: 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
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