2025
A Generative Artificial Intelligence Copilot for Biomedical Nanoengineering
Wang Y, Song H, Teng Y, Huang G, Qian J, Wang H, Dong S, Ha J, Ma Y, Chang M, Jeong S, Deng W, Schrank B, Grippin A, Wu A, Edwards J, Zhang Y, Lin Y, Poon W, Wilhelm S, Bi Y, Teng L, Wang Z, Kim B, Jiang W. A Generative Artificial Intelligence Copilot for Biomedical Nanoengineering. ACS Nano 2025, 19: 19394-19407. PMID: 40367350, DOI: 10.1021/acsnano.5c03454.Peer-Reviewed Original ResearchConceptsArtificial intelligenceNatural language processing tasksExtract contextual informationLanguage processing tasksAutomatically extract knowledgeAI-based methodsGenerative artificial intelligenceInformation extractionLanguage modelAutomated learningContextual informationProcessing tasksIntelligent copilotBaseline modelAI toolsDesign tasksTaskQueryScientific queriesAutomaticallyCopilotIntelligenceScientific researchLLMLearningBiomedRAG: A retrieval augmented large language model for biomedicine
Li M, Kilicoglu H, Xu H, Zhang R. BiomedRAG: A retrieval augmented large language model for biomedicine. Journal Of Biomedical Informatics 2025, 162: 104769. PMID: 39814274, PMCID: PMC11837810, DOI: 10.1016/j.jbi.2024.104769.Peer-Reviewed Original Research
2024
The “ David Vs Goliath ” Study: Application of Large Language Models (LLM) for Automatic Medical Information Retrieval from Multiple Data Sources to Accelerate Clinical and Translational Research in Hematology
Delleani M, D'Amico S, Sauta E, Asti G, Zazzetti E, Campagna A, Lanino L, Maggioni G, Grondelli M, Forcina Barrero A, Morandini P, Ubezio M, Todisco G, Russo A, Tentori C, Buizza A, Bonometti A, Lancellotti C, Di Tommaso L, Rahal D, Bicchieri M, Savevski V, Santoro A, Santini V, Sole F, Platzbecker U, Fenaux P, Diez-Campelo M, Komrokji R, Garcia-Manero G, Haferlach T, Kordasti S, Zeidan A, Castellani G, Della Porta M. The “ David Vs Goliath ” Study: Application of Large Language Models (LLM) for Automatic Medical Information Retrieval from Multiple Data Sources to Accelerate Clinical and Translational Research in Hematology. Blood 2024, 144: 3597-3597. DOI: 10.1182/blood-2024-205621.Peer-Reviewed Original ResearchGenerative Pretrained TransformerInformation retrievalHealthcare dataLanguage modelArtificial intelligenceNatural language processing tasksSemi-supervised training processMedical information retrievalAutomatic information retrievalOriginal datasetLanguage processing tasksValidation frameworkData collection tasksRetrieval information systemsReducing human effortPotential of artificial intelligenceLearning statistical relationshipsStatistical fidelitySelf-supervisionPretrained TransformerStandard datasetsLanguage generationPrivacy limitationsData model formatCollection tasksA Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation
Wen A, Wang L, He H, Fu S, Liu S, Hanauer D, Harris D, Kavuluru R, Zhang R, Natarajan K, Pavinkurve N, Hajagos J, Rajupet S, Lingam V, Saltz M, Elowsky C, Moffitt R, Koraishy F, Palchuk M, Donovan J, Lingrey L, Stone-DerHagopian G, Miller R, Williams A, Leese P, Kovach P, Pfaff E, Zemmel M, Pates R, Guthe N, Haendel M, Chute C, Liu H, Collaborative C, Initiative T. A Case Demonstration of the Open Health Natural Language Processing Toolkit From the National COVID-19 Cohort Collaborative and the Researching COVID to Enhance Recovery Programs for a Natural Language Processing System for COVID-19 or Postacute Sequelae of SARS CoV-2 Infection: Algorithm Development and Validation. JMIR Medical Informatics 2024, 12: e49997. PMID: 39250782, PMCID: PMC11420592, DOI: 10.2196/49997.Peer-Reviewed Original ResearchNatural language processingNatural language processing algorithmsNatural language processing toolkitNatural language processing systemsProcessing toolkitNatural language processing tasksUnified Medical Language SystemClinical natural language processingAlgorithm developmentMedical Language SystemLanguage processing systemDevelopment approachNLP tasksTime-critical natureHuman expertsNatural language processing resultsExpert annotationsLanguage processingTraining setClinical narrativesNatural language processing extractsTest setAlgorithmPostacute sequelae of SARS-CoV-2 infectionProcessing systemKamino: A Scalable Architecture to Support Medical AI Research Using Large Real World Data
Lin F, Young P, He H, Huang J, Gagne R, Rice D, Price N, Byron W, Hu Y, Felker D, Button W, Meeker D, Hsiao A, Xu H, Torre C, Schulz W. Kamino: A Scalable Architecture to Support Medical AI Research Using Large Real World Data. 2024, 00: 500-504. DOI: 10.1109/ichi61247.2024.00072.Peer-Reviewed Original ResearchElectronic health recordsAI researchNatural language processing tasksElectronic health record dataLanguage processing tasksComputing resource managementLarge-scale data retrievalMedical AI researchLeveraging electronic health recordsStandard data modelKubernetes orchestratorScalable architectureProcessing tasksResource allocation systemsSecurity considerationsAccess managementData retrievalData modelArchitectural solutionsOMOP CDMReal World DataWorld dataHealth recordsOMOPDataA Study of Biomedical Relation Extraction Using GPT Models.
Zhang J, Wibert M, Zhou H, Peng X, Chen Q, Keloth V, Hu Y, Zhang R, Xu H, Raja K. A Study of Biomedical Relation Extraction Using GPT Models. AMIA Joint Summits On Translational Science Proceedings 2024, 2024: 391-400. PMID: 38827097, PMCID: PMC11141827.Peer-Reviewed Original Research
2023
Improving model transferability for clinical note section classification models using continued pretraining
Zhou W, Yetisgen M, Afshar M, Gao Y, Savova G, Miller T. Improving model transferability for clinical note section classification models using continued pretraining. Journal Of The American Medical Informatics Association 2023, 31: 89-97. PMID: 37725927, PMCID: PMC10746297, DOI: 10.1093/jamia/ocad190.Peer-Reviewed Original ResearchConceptsClinical note sectionsIn-domainClassification modelNatural language processing tasksNeural network-based methodTemporal information extractionLanguage processing tasksDrop of accuracyBERT-based modelsNetwork-based methodsInformation extractionCross-domainModel transferabilityF1 scoreProcessing tasksSocial determinantsBaseline modelPretrainingClassificationImprove model transferabilityNotes sectionModel performanceAccuracyImproved modelDataset
2022
ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records
Wei Q, Zuo X, Anjum O, Hu Y, Denlinger R, Bernstam E, Citardi M, Xu H. ClinicalLayoutLM: A Pre-trained Multi-modal Model for Understanding Scanned Document in Electronic Health Records. 2022, 00: 2821-2827. DOI: 10.1109/bigdata55660.2022.10020569.Peer-Reviewed Original ResearchOptical character recognitionMulti-modal modelElectronic health recordsClinical documentsNatural language processing tasksInformation extraction technologyPre-trained modelsHealth recordsLanguage processing tasksInformation extractionImage informationF1 scoreCharacter recognitionLayout analysisProcessing tasksMulti-modal approachClinical corpusBaseline modelDocumentsOpen domainTaskExtraction technologyClinical operationsDifferent categoriesTextClinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types.
Eisman A, Brown K, Chen E, Sarkar I. Clinical Note Section Detection Using a Hidden Markov Model of Unified Medical Language System Semantic Types. AMIA Annual Symposium Proceedings 2022, 2021: 418-427. PMID: 35308919, PMCID: PMC8861726.Peer-Reviewed Original ResearchConceptsNatural language processingUnified Medical Language SystemNatural language processing tasksMedical Language SystemSources of biomedical dataClinical note sectionsUnified Medical Language System semantic typesHidden Markov ModelNLP toolsLanguage processingBiomedical dataSection detectionClinical notesSemantic typesHMMLanguage systemMedical Information Mart for Intensive Care III
2019
Enhancing clinical concept extraction with contextual embeddings
Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. Journal Of The American Medical Informatics Association 2019, 26: 1297-1304. PMID: 31265066, PMCID: PMC6798561, DOI: 10.1093/jamia/ocz096.Peer-Reviewed Original ResearchConceptsClinical concept extractionContextual embeddingsNatural language processing tasksTraditional word embeddingsTraditional word representationsClinical NLP tasksLanguage processing tasksSemantic informationWord embedding methodsLarge language modelsArt performanceConcept extraction taskSemEval 2014Word representationsNLP tasksLanguage modelWord embeddingsProcessing tasksNeural network-based representationI2b2 2010Concept extractionTaskLarge clinical corpusClinical corpusNetwork-based representation
2018
Clinical Named Entity Recognition Using Deep Learning Models.
Wu Y, Jiang M, Xu J, Zhi D, Xu H. Clinical Named Entity Recognition Using Deep Learning Models. AMIA Annual Symposium Proceedings 2018, 2017: 1812-1819. PMID: 29854252, PMCID: PMC5977567.Peer-Reviewed Original ResearchConceptsClinical Named Entity RecognitionNamed Entity RecognitionDeep learning modelsConvolutional neural networkClinical NER systemRecurrent neural networkNeural networkLearning modelEntity recognitionRNN modelNER systemDeep neural network architecturePopular deep learning architecturesNatural language processing tasksUnsupervised learning featuresConditional random field modelAutomatic feature learningDeep learning architectureClinical NER tasksDeep neural networksNeural network architectureClinical concept extractionLanguage processing tasksFeature learningLearning architecture
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