2022
Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data
Rasmy L, Nigo M, Kannadath B, Xie Z, Mao B, Patel K, Zhou Y, Zhang W, Ross A, Xu H, Zhi D. Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. The Lancet Digital Health 2022, 4: e415-e425. PMID: 35466079, PMCID: PMC9023005, DOI: 10.1016/s2589-7500(22)00049-8.Peer-Reviewed Original ResearchConceptsLight Gradient Boost MachineFeature engineeringGradient-boosting machineMultiple machine learning modelsElectronic health record dataNeural network-based modelReal-world datasetsRecurrent neural network modelComplex feature engineeringMachine learning modelsBinary classification taskSpecific feature selectionLogistic regression algorithmNeural network modelHealth record dataRecurrent neural network-based modelBinary classification modelNetwork-based modelTraditional machineExtensive data preprocessingHigh prediction accuracyMultiple external datasetsClassification taskData preprocessingFeature selection
2020
Relation Extraction from Clinical Narratives Using Pre-trained Language Models.
Wei Q, Ji Z, Si Y, Du J, Wang J, Tiryaki F, Wu S, Tao C, Roberts K, Xu H. Relation Extraction from Clinical Narratives Using Pre-trained Language Models. AMIA Annual Symposium Proceedings 2020, 2019: 1236-1245. PMID: 32308921, PMCID: PMC7153059.Peer-Reviewed Original ResearchConceptsPre-trained language modelsNatural language processingLanguage modelRE tasksNLP tasksClinical narrativesRecent deep learning methodsDeep learning methodsClinical NLP tasksRelation extraction taskTraditional word embeddingsTraditional machineExtraction taskArt performanceRelation extractionBERT modelLanguage processingLearning methodsWord embeddingsShared TaskPrevious stateBiomedical literatureDifferent implementationsTaskOpen domain
2019
A study of deep learning approaches for medication and adverse drug event extraction from clinical text
Wei Q, Ji Z, Li Z, Du J, Wang J, Xu J, Xiang Y, Tiryaki F, Wu S, Zhang Y, Tao C, Xu H. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. Journal Of The American Medical Informatics Association 2019, 27: 13-21. PMID: 31135882, PMCID: PMC6913210, DOI: 10.1093/jamia/ocz063.Peer-Reviewed Original ResearchConceptsDeep learning-based approachDeep learning approachLearning-based approachTraditional machineLearning approachNational NLP Clinical ChallengesAdverse drug event extractionOutperform traditional machineDifferent ensemble approachesConditional Random FieldsSequence labeling approachMIMIC-III databaseEvent extractionMedical domainEntity recognitionClassification componentF1 scoreClinical textRelation extractionClinical documentsVector machineEnd evaluationEnsemble approachClinical corpusMachine
2018
Extracting psychiatric stressors for suicide from social media using deep learning
Du J, Zhang Y, Luo J, Jia Y, Wei Q, Tao C, Xu H. Extracting psychiatric stressors for suicide from social media using deep learning. BMC Medical Informatics And Decision Making 2018, 18: 43. PMID: 30066665, PMCID: PMC6069295, DOI: 10.1186/s12911-018-0632-8.Peer-Reviewed Original ResearchConceptsConvolutional neural networkRecurrent neural networkDeep learningConditional Random FieldsSupport vector machineSuicide-related tweetsClinical textNeural networkPsychiatric stressorsExtra TreesBinary classifierTransfer learning strategiesEntity recognition taskSocial mediaExact matchTraditional machineAnnotation costLearning strategiesRecognition problemSharing flowInexact matchVector machineTwitter dataRecognition taskTwitter
2017
Entity recognition from clinical texts via recurrent neural network
Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, Xu H. Entity recognition from clinical texts via recurrent neural network. BMC Medical Informatics And Decision Making 2017, 17: 67. PMID: 28699566, PMCID: PMC5506598, DOI: 10.1186/s12911-017-0468-7.Peer-Reviewed Original ResearchConceptsRecurrent neural networkNatural language processingEntity recognitionClinical textTraditional machineNeural networkClinical natural language processingMedical concept extractionHand-crafted featuresClinical entity recognitionDeep learning methodsClinical event detectionConditional Random FieldsSupport vector machineI2b2 NLP challengePerformance of LSTMTypes of entitiesClinical domainsContext informationFeature engineeringConcept extractionDe-identificationEvent detectionKnowledge basesLSTM layers