2024
A machine learning framework to adjust for learning effects in medical device safety evaluation
Koola J, Ramesh K, Mao J, Ahn M, Davis S, Govindarajulu U, Perkins A, Westerman D, Ssemaganda H, Speroff T, Ohno-Machado L, Ramsay C, Sedrakyan A, Resnic F, Matheny M. A machine learning framework to adjust for learning effects in medical device safety evaluation. Journal Of The American Medical Informatics Association 2024, 32: 206-217. PMID: 39471493, PMCID: PMC11648715, DOI: 10.1093/jamia/ocae273.Peer-Reviewed Original ResearchMachine Learning FrameworkSynthetic datasetsLearning frameworkMachine learningCapacity of MLLearning effectFeature correlationDepartment of Veterans AffairsSynthetic dataData generationAbsence of learning effectsTraditional statistical methodsML methodsSuperior performanceDatasetSafety signal detectionSignal detectionDevice signalsVeterans AffairsTime-varying covariatesLearningMachinePhysician experienceLimitations of traditional statistical methodsMedical device post-market surveillanceThe influence of psilocybin on subconscious and conscious emotional learning
Casanova A, Ort A, Smallridge J, Preller K, Seifritz E, Vollenweider F. The influence of psilocybin on subconscious and conscious emotional learning. IScience 2024, 27: 110034. PMID: 38883812, PMCID: PMC11177198, DOI: 10.1016/j.isci.2024.110034.Peer-Reviewed Original ResearchPsychedelic-Assisted PsychotherapyAgonist psilocybinFearful facesNeutral cuesEmotional cuesPsychiatric disordersSerotonergic psychedelicsSerotonin SignalingExploratory behaviorPsilocybinEmotional learningLearning effectCuesPlacebo groupPlaceboPsychotherapyLearning rateLearningPsychedelicsSerotoninReinforcement learningDisordersTreatment modalitiesBrainTask
2023
Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance
Davis S, Ssemaganda H, Koola J, Mao J, Westerman D, Speroff T, Govindarajulu U, Ramsay C, Sedrakyan A, Ohno-Machado L, Resnic F, Matheny M. Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance. BMC Medical Research Methodology 2023, 23: 89. PMID: 37041457, PMCID: PMC10088292, DOI: 10.1186/s12874-023-01913-9.Peer-Reviewed Original ResearchConceptsSynthetic datasetsData characteristicsFeature distributionGround truthMIMIC-III dataReal-world dataData generation processComplex simulation studiesData relationshipsUser definitionSmall datasetsSimulation requirementsCorrelated featuresWorld dataCustomizable optionsReal-world complexitySynthetic patientsNew algorithmDatasetGeneration processLearningAlgorithmData simulation techniquesLearning effectGeneralizable framework
2019
Robots for Learning - R4L: Adaptive Learning
Johal W, Sandygulova A, de Wit J, de Haas M, Scassellati B. Robots for Learning - R4L: Adaptive Learning. 2019, 00: 693-694. DOI: 10.1109/hri.2019.8673109.Peer-Reviewed Original Research
2016
Implicit associative learning in synesthetes and nonsynesthetes
Bankieris KR, Aslin RN. Implicit associative learning in synesthetes and nonsynesthetes. Psychonomic Bulletin & Review 2016, 24: 935-943. PMID: 27612860, PMCID: PMC5344781, DOI: 10.3758/s13423-016-1162-y.Peer-Reviewed Original ResearchConceptsImplicit associative learningShape-colour associationsAssociative learningLearning effectDevelopment of synesthesiaLong-term memoryMost current theoriesMemory differSynesthetic associationsNonsynesthetesSynesthetesGreater interferenceCurrent theoriesReaction timeLearningContinuous measureNeural connectionsMultiple time pointsSynesthesiaGenetic underpinningsMemoryTarget detectionUnderpinningsParticipantsAssociation
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply