2024
Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets
Li Y, Tao W, Li Z, Sun Z, Li F, Fenton S, Xu H, Tao C. Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets. Journal Of Biomedical Informatics 2024, 152: 104621. PMID: 38447600, DOI: 10.1016/j.jbi.2024.104621.Peer-Reviewed Original ResearchNamed-entity recognitionEnd-to-end tasksEnd-to-endMachine learningBenchmark datasetsAdverse drug event extractionNamed-entity recognition taskLearning modelsAdverse drug event detectionBidirectional Encoder RepresentationsDeep learning techniquesDeep learning methodsDeep learning modelsEffectiveness of machine learningDeep learning methodologyMachine learning modelsSocial media dataEncoder RepresentationsEvent detectionDeep learningLearning techniquesMultilayer perceptronLearning methodsMedia dataRC task
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