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Machine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation

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dc.contributor.author강바다-
dc.contributor.author조은희-
dc.contributor.author허석재-
dc.date.accessioned2023-07-12T03:15:18Z-
dc.date.available2023-07-12T03:15:18Z-
dc.date.issued2023-05-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/195550-
dc.description.abstractThe behavioral and psychological symptoms of dementia (BPSD) are challenging aspects of dementia care. This study used machine learning models to predict the occurrence of BPSD among community-dwelling older adults with dementia. We included 187 older adults with dementia for model training and 35 older adults with dementia for external validation. Demographic and health data and premorbid personality traits were examined at the baseline, and actigraphy was utilized to monitor sleep and activity levels. A symptom diary tracked caregiver-perceived symptom triggers and the daily occurrence of 12 BPSD classified into seven subsyndromes. Several prediction models were also employed, including logistic regression, random forest, gradient boosting machine, and support vector machine. The random forest models revealed the highest area under the receiver operating characteristic curve (AUC) values for hyperactivity, euphoria/elation, and appetite and eating disorders; the gradient boosting machine models for psychotic and affective symptoms; and the support vector machine model showed the highest AUC. The gradient boosting machine model achieved the best performance in terms of average AUC scores across the seven subsyndromes. Caregiver-perceived triggers demonstrated higher feature importance values across the seven subsyndromes than other features. Our findings demonstrate the possibility of predicting BPSD using a machine learning approach.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.relation.isPartOfSCIENTIFIC REPORTS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAged-
dc.subject.MESHBehavioral Symptoms*-
dc.subject.MESHDementia* / psychology-
dc.subject.MESHHumans-
dc.subject.MESHLogistic Models-
dc.subject.MESHMachine Learning-
dc.subject.MESHROC Curve-
dc.titleMachine learning-based predictive models for the occurrence of behavioral and psychological symptoms of dementia: model development and validation-
dc.typeArticle-
dc.contributor.collegeCollege of Nursing (간호대학)-
dc.contributor.departmentDept. of Nursing (간호학과)-
dc.contributor.googleauthorEunhee Cho-
dc.contributor.googleauthorSujin Kim-
dc.contributor.googleauthorSeok-Jae Heo-
dc.contributor.googleauthorJinhee Shin-
dc.contributor.googleauthorSinwoo Hwang-
dc.contributor.googleauthorEunji Kwon-
dc.contributor.googleauthorSungHee Lee-
dc.contributor.googleauthorSangGyun Kim-
dc.contributor.googleauthorBada Kang-
dc.identifier.doi10.1038/s41598-023-35194-5-
dc.contributor.localIdA06199-
dc.contributor.localIdA03886-
dc.relation.journalcodeJ02646-
dc.identifier.eissn2045-2322-
dc.identifier.pmid37202454-
dc.contributor.alternativeNameKang, Bada-
dc.contributor.affiliatedAuthor강바다-
dc.contributor.affiliatedAuthor조은희-
dc.citation.volume13-
dc.citation.number1-
dc.citation.startPage8073-
dc.identifier.bibliographicCitationSCIENTIFIC REPORTS, Vol.13(1) : 8073, 2023-05-
Appears in Collections:
3. College of Nursing (간호대학) > Dept. of Nursing (간호학과) > 1. Journal Papers

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