2024
Developing 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
2023
Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models
Li Z, Ameer I, Hu Y, Abdelhameed A, Tao C, Selek S, Xu H. Suicide Tendency Prediction from Psychiatric Notes Using Transformer Models. 2023, 00: 481-483. DOI: 10.1109/ichi57859.2023.00074.Peer-Reviewed Original ResearchWeighted F1 scoreF1 scoreMachine learning modelsElectronic health recordsLearning modelsState-of-the-art modelsState-of-the-artBinary classification taskHealth recordsBinary classification modelStandard diagnosis codesClassification taskMulticlass classificationHealth informaticsClassification modelMental health informaticsTransformation modelPrediction algorithmPsychiatric notesInitial psychiatric evaluationSuicidal tendenciesMachineRandom forest modelSuicidal ideationPerformance