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 progressChallengesBioREx: 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 researchDataset
2019
ML-Net: multi-label classification of biomedical texts with deep neural networks
Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. Journal Of The American Medical Informatics Association 2019, 26: 1279-1285. PMID: 31233120, PMCID: PMC7647240, DOI: 10.1093/jamia/ocz085.Peer-Reviewed Original ResearchConceptsMulti-label classificationML-NetBiomedical textEnd deep learning frameworkMulti-label text classificationDeep learning frameworkDeep neural networksTraditional machineDocument contextFeature engineeringText classificationTextual documentsMachine learningNovel endLearning frameworkPrediction networkIndividual classifiersNeural networkHuman effortTarget documentsF-measureArt methodsPrediction mechanismContextual informationLabel counts