Modeling user choice behavior under data corruption: Robust learning of the latent decision threshold model
Lin F, Qian X, Mortazavi B, Wang Z, Huang S, Chen C. Modeling user choice behavior under data corruption: Robust learning of the latent decision threshold model. IISE Transactions 2023, 56: 1307-1320. DOI: 10.1080/24725854.2023.2279080.Peer-Reviewed Original ResearchData corruptionReal-world user dataUser-centered systemsRobust learning frameworkRobust learning methodNew mobile appUser dataUser behaviorLearning frameworkLearning methodsArt methodsMobile appsRobust learningUsers' choice behaviorPrediction accuracyBad actorsUsersNew applicationsConsiderable research effortFrameworkResearch effortsModel estimationRecent yearsAlgorithmAppsPredicting Real-time, Recurrent Adverse Invasive Ventilation from Clinical Data Streams
Pakbin A, Nowroozilarki Z, Lee D, Mortazavi B. Predicting Real-time, Recurrent Adverse Invasive Ventilation from Clinical Data Streams. 2023, 00: 1-4. DOI: 10.1109/bsn58485.2023.10331225.Peer-Reviewed Original ResearchMachine learning methodsClinical data streamsReal-time risk monitoringNon-recurring eventsData streamsLearning methodsData changesEHR dataIntensive care unit patientsElectronic health record dataCare unit patientsReal-time monitoringAnalysis toolsHealth record dataQuality of careEvent dataICU stayInvasive ventilationTime-dependent covariatesUnit patientsWarning systemTremendous opportunitiesRisk monitoringSurvival analysisPersonalized treatment