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
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
2011
A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
Jiang M, Chen Y, Liu M, Rosenbloom S, Mani S, Denny J, Xu H. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal Of The American Medical Informatics Association 2011, 18: 601-606. PMID: 21508414, PMCID: PMC3168315, DOI: 10.1136/amiajnl-2011-000163.Peer-Reviewed Original ResearchConceptsEntity extraction systemCenter of InformaticsConcept extractionIntegrating BiologyEntity recognition moduleEntity recognition systemConditional Random FieldsOverall F-scoreSupport vector machineRule-based moduleAssertion classificationClassification taskRecognition moduleRecognition systemML algorithmsSemantic informationTraining dataClinical textNatural languageF-measureChallenge organizersF-scoreVector machineEvaluation scriptsTraining corpus