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
2015
Ease of adoption of clinical natural language processing software: An evaluation of five systems
Zheng K, Vydiswaran V, Liu Y, Wang Y, Stubbs A, Uzuner Ö, Gururaj A, Bayer S, Aberdeen J, Rumshisky A, Pakhomov S, Liu H, Xu H. Ease of adoption of clinical natural language processing software: An evaluation of five systems. Journal Of Biomedical Informatics 2015, 58: s189-s196. PMID: 26210361, PMCID: PMC4974203, DOI: 10.1016/j.jbi.2015.07.008.Peer-Reviewed Original ResearchConceptsClinical NLP systemsNLP systemsNatural language processing softwareThird-party componentsUsability testing toolGroup of usersLanguage processing softwareEase of adoptionExpert evaluatorsSoftware distributionBiomedical softwareComputer scienceEnd usersUsability assessmentI2b2 challengeTesting toolsEvaluation showHuman evaluatorsSystem submissionsEase of useHealth informaticsProcessing softwareAdoption issuesUsersSpecial track