Machine learning-based screening for outpatients with dementia using drawing features from the clock drawing test.

IF 3 3区 心理学 Q2 CLINICAL NEUROLOGY Clinical Neuropsychologist Pub Date : 2024-10-22 DOI:10.1080/13854046.2024.2413555
Akira Masuo, Junpei Kubota, Katsuhiko Yokoyama, Kaori Karaki, Hiroyuki Yuasa, Yuki Ito, Jun Takeo, Takuto Sakuma, Shohei Kato
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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.

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基于机器学习的痴呆症门诊患者筛查,利用时钟绘图测试的绘图特征。
背景和目的:在老年医学和痴呆症护理中,早期诊断至关重要。我们利用时钟绘画测试(CDT)的绘画特征建立了痴呆症筛查模型,并研究了有助于辨别痴呆症的特征及其筛查性能。研究方法这项研究包括 129 名到痴呆症门诊就诊的老年人。我们从病历中获取了痴呆诊断信息和 CDT 数据,并根据弗里德曼评分系统对 12 种绘画特征进行了量化。根据痴呆症诊断信息,我们将参与者分为两组:58 人在痴呆症诊断组,71 人在非诊断组。我们使用迭代特征选择算法 Boruta 和机器学习方法支持向量机分析了有助于鉴别痴呆症的绘画特征,并评估了鉴别效果。结果如下我们选出了五种有助于辨别的绘画特征,包括 "数字在正确的位置"、"指示的分钟目标数字 "和 "手的比例正确"。这些特征在检测痴呆症方面的辨别灵敏度为 0.74 ± 0.16,特异度为 0.74 ± 0.18。结论本研究展示了一种利用绘画特征识别痴呆症门诊患者中可能被诊断为痴呆症患者的方法。对有助于区分痴呆症的绘画特征的了解可能有助于医疗从业人员进行临床推理,并为临床实践提供新的见解。未来,我们计划利用 CDT 开发一种基于机器学习的痴呆症初筛方法。
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来源期刊
Clinical Neuropsychologist
Clinical Neuropsychologist 医学-临床神经学
CiteScore
8.40
自引率
12.80%
发文量
61
审稿时长
6-12 weeks
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.
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