2024
Improving tabular data extraction in scanned laboratory reports using deep learning models
Li Y, Wei Q, Chen X, Li J, Tao C, Xu H. Improving tabular data extraction in scanned laboratory reports using deep learning models. Journal Of Biomedical Informatics 2024, 159: 104735. PMID: 39393477, DOI: 10.1016/j.jbi.2024.104735.Peer-Reviewed Original ResearchTree edit distanceOptical character recognitionTable recognitionDeep learning modelsAverage recallAverage precisionState-of-the-art deep learning modelsLearning modelsRegion-of-interest detectionState-of-the-artCharacter recognitionDetection evaluationTree editingTabular dataImpressive resultsLab test resultsLaboratory test reportsClinical documentationRecognitionLaboratory reportsHealthcare organizationsClinical data analysisDecision makingClinical decision makingTest reportsDeveloping deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records
Li Z, Lan L, Zhou Y, Li R, Chavin K, Xu H, Li L, Shih D, Zheng W. Developing deep learning-based strategies to predict the risk of hepatocellular carcinoma among patients with nonalcoholic fatty liver disease from electronic health records. Journal Of Biomedical Informatics 2024, 152: 104626. PMID: 38521180, DOI: 10.1016/j.jbi.2024.104626.Peer-Reviewed Original ResearchDeep learning modelsElectronic health recordsHCC risk predictionHealth recordsTime-varying covariatesLearning modelsElectronic health record dataRisk predictionHealth record dataAccuracy of deep learning modelsDeep learning-based strategyCovariate imbalanceDisease prediction tasksLearning-based strategyDeep learning performanceDisease risk predictionEHR databaseClassification problemLength of follow-upTransfer learningFatty liver diseasePrediction taskCarcinoma riskModel trainingRecord dataArtificial 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
2020
Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study
Xiang Y, Ji H, Zhou Y, Li F, Du J, Rasmy L, Wu S, Zheng W, Xu H, Zhi D, Zhang Y, Tao C. Asthma Exacerbation Prediction and Risk Factor Analysis Based on a Time-Sensitive, Attentive Neural Network: Retrospective Cohort Study. Journal Of Medical Internet Research 2020, 22: e16981. PMID: 32735224, PMCID: PMC7428917, DOI: 10.2196/16981.Peer-Reviewed Original ResearchConceptsAttentive Neural NetworkAsthma exacerbationsRisk factorsNeural networkAdvanced deep learning modelsClinical variablesDeep learning modelsCerner Health Facts databaseLarge electronic health recordNeural network modelRetrospective cohort studyHealth Facts databasePotential risk factorsRisk factor analysisPersonalized risk factorsElectronic health recordsBaseline methodsLearning modelPersonalized risk scoreProgressive asthmaAsthma symptomsEsophageal refluxAdult patientsCohort studyTime-Sensitive
2018
Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition.
Wu Y, Yang X, Bian J, Guo Y, Xu H, Hogan W. Combine Factual Medical Knowledge and Distributed Word Representation to Improve Clinical Named Entity Recognition. AMIA Annual Symposium Proceedings 2018, 2018: 1110-1117. PMID: 30815153, PMCID: PMC6371322.Peer-Reviewed Original ResearchConceptsRecurrent neural networkWord embeddingsOne-hot vectorsWord representationsLow-frequency wordsOnly word embeddingsClinical Named Entity RecognitionClinical NER tasksWord embedding methodsConditional Random FieldsStatistical language modelNamed Entity RecognitionUnlabeled corpusLanguage modelLanguage systemNER taskDecent representationFactual medical knowledgeImportant wordsDeep learning modelsEntity recognitionClinical corpusNamed Entity Recognition SystemArt performanceFeature representationA study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set
Rasmy L, Wu Y, Wang N, Geng X, Zheng W, Wang F, Wu H, Xu H, Zhi D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. Journal Of Biomedical Informatics 2018, 84: 11-16. PMID: 29908902, PMCID: PMC6076336, DOI: 10.1016/j.jbi.2018.06.011.Peer-Reviewed Original ResearchConceptsRecurrent neural networkOnset riskCapability of RNNCerner Health FactsHeterogeneous EHR dataHeart failure patientsData setsElectronic health record dataDeep learning modelsDifferent patient populationsNeural network-based predictive modelDifferent patient groupsHealth record dataEHR data setsPredictive modelingSmall data setsFailure patientsPatient groupPatient populationReduction of AUCNeural networkRNN modelRETAIN modelHealth FactsHospitalClinical 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