The Clock Drawing Test—a simple pencil and paper test—has been used for more than 50 years as a screening tool to differentiate normal individuals from those with cognitive impairment, and has proven useful in helping to diagnose cognitive dysfunction associated with neurological disorders such as Alzheimer’s disease, Parkinson’s disease, and other dementias and conditions. We have been administering the test using a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making available far more detailed data about the subject’s performance. Using pen stroke data from these drawings categorized by our software, we designed and computed a large collection of features, then explored the tradeoffs in performance and interpretability in classifiers built using a number of different subsets of these features and a variety of different machine learning techniques. We used traditional machine learning methods to build prediction models that achieve high accuracy. We operationalized widely used manual scoring systems so that we could use them as benchmarks for our models. We worked with clinicians to define guidelines for model interpretability, and constructed sparse linear models and rule lists designed to be as easy to use as scoring systems currently used by clinicians, but more accurate. While our models will require additional testing for validation, they offer the possibility of substantial improvement in detecting cognitive impairment earlier than currently possible, a development with considerable potential impact in practice.

中文翻译：

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从数字时钟绘图测试中的微妙行为中学习认知条件的分类模型

时钟绘图测试 - 一种简单的铅笔和纸测试 - 已作为一种筛选工具使用了 50 多年，以区分正常人和有认知障碍的人，并已证明有助于诊断与神经系统疾病相关的认知功能障碍，例如阿尔茨海默病、帕金森病和其他痴呆症和病症。我们一直使用数字化圆珠笔进行测试，该笔以相当大的空间和时间精度报告其位置，从而提供有关受试者表现的更详细的数据。使用由我们的软件分类的这些绘图中的笔划数据，我们设计并计算了大量特征，然后探讨了使用这些特征的许多不同子集和各种不同机器学习技术构建的分类器在性能和可解释性方面的权衡。我们使用传统的机器学习方法来构建实现高精度的预测模型。我们操作了广泛使用的手动评分系统，以便我们可以将它们用作我们模型的基准。我们与临床医生合作定义了模型可解释性指南，并构建了稀疏线性模型和规则列表，旨在与临床医生目前使用的评分系统一样易于使用，但更准确。虽然我们的模型需要额外的验证测试，但它们提供了比目前更早地检测认知障碍的实质性改进的可能性，