{"title":"Machine learning-based screening for outpatients with dementia using drawing features from the clock drawing test.","authors":"Akira Masuo, Junpei Kubota, Katsuhiko Yokoyama, Kaori Karaki, Hiroyuki Yuasa, Yuki Ito, Jun Takeo, Takuto Sakuma, Shohei Kato","doi":"10.1080/13854046.2024.2413555","DOIUrl":null,"url":null,"abstract":"<p><p><b>Background and Objectives:</b> In geriatrics and dementia care, early diagnosis is crucial. We developed a dementia screening model using drawing features from clock drawing tests (CDT) and investigated the features contributing to the discrimination of dementia and its screening performance. <b>Methods:</b> This study included 129 older adults attending a dementia outpatient clinic. We obtained information on the diagnosis of dementia and CDT data from medical records and quantified 12 types of drawing features according to the Freedman scoring system. Based on the dementia diagnosis information, participants were assigned to two groups: 58 in the dementia diagnosis group and 71 in the non-diagnosis group. Using Boruta, an iterative feature selection algorithm, and a support vector machine, a machine learning method, we analyzed the drawing features contributing to dementia discrimination and evaluated discrimination performance. <b>Results:</b> Five types of drawing features were selected as contributors to discrimination, including \"numbers in the correct position,\" \"minute target number indicated,\" and \"hand in correct proportion.\" These features exhibited a discriminating sensitivity of 0.74 ± 0.16 and specificity of 0.74 ± 0.18 for detecting dementia. <b>Conclusion:</b> This study demonstrated a method for identifying individuals likely to be diagnosed with dementia among patients attending a dementia outpatient clinic using drawing features. The knowledge of drawing features contributing to dementia differentiation may assist healthcare practitioners in clinical reasoning and provide novel insights for clinical practice. In the future, we plan to develop a primary screening for dementia based on machine learning using CDT.</p>","PeriodicalId":55250,"journal":{"name":"Clinical Neuropsychologist","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Neuropsychologist","FirstCategoryId":"102","ListUrlMain":"https://doi.org/10.1080/13854046.2024.2413555","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background and Objectives: In geriatrics and dementia care, early diagnosis is crucial. We developed a dementia screening model using drawing features from clock drawing tests (CDT) and investigated the features contributing to the discrimination of dementia and its screening performance. Methods: This study included 129 older adults attending a dementia outpatient clinic. We obtained information on the diagnosis of dementia and CDT data from medical records and quantified 12 types of drawing features according to the Freedman scoring system. Based on the dementia diagnosis information, participants were assigned to two groups: 58 in the dementia diagnosis group and 71 in the non-diagnosis group. Using Boruta, an iterative feature selection algorithm, and a support vector machine, a machine learning method, we analyzed the drawing features contributing to dementia discrimination and evaluated discrimination performance. Results: Five types of drawing features were selected as contributors to discrimination, including "numbers in the correct position," "minute target number indicated," and "hand in correct proportion." These features exhibited a discriminating sensitivity of 0.74 ± 0.16 and specificity of 0.74 ± 0.18 for detecting dementia. Conclusion: This study demonstrated a method for identifying individuals likely to be diagnosed with dementia among patients attending a dementia outpatient clinic using drawing features. The knowledge of drawing features contributing to dementia differentiation may assist healthcare practitioners in clinical reasoning and provide novel insights for clinical practice. In the future, we plan to develop a primary screening for dementia based on machine learning using CDT.
期刊介绍:
The Clinical Neuropsychologist (TCN) serves as the premier forum for (1) state-of-the-art clinically-relevant scientific research, (2) in-depth professional discussions of matters germane to evidence-based practice, and (3) clinical case studies in neuropsychology. Of particular interest are papers that can make definitive statements about a given topic (thereby having implications for the standards of clinical practice) and those with the potential to expand today’s clinical frontiers. Research on all age groups, and on both clinical and normal populations, is considered.