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 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 scaleAIONER: 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 scarcityTaskLearning
2020
Deep 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 set