Neurocast B.V., Amsterdam, The Netherlands.
Department of Neurology, Amsterdam University Medical Centers, Amsterdam, The Netherlands.
Sci Rep. 2023 Feb 1;13(1):1871. doi: 10.1038/s41598-023-28990-6.
Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation and inaccuracies coming from objective and self-reported evaluations limits these assessments. Alternatively, remote health monitoring through devices can offer a cost-efficient solution to gather more reliable, objective measures continuously. Measuring smartphone keyboard interactions is a promising tool since typing and, thus, keystroke dynamics are likely influenced by symptoms that pwMS can experience. Therefore, this paper aims to investigate whether keyboard interactions gathered on a person's smartphone can provide insight into the clinical status of pwMS leveraging machine learning techniques. In total, 24 Healthy Controls (HC) and 102 pwMS were followed for one year. Next to continuous data generated via smartphone interactions, clinical outcome measures were collected and used as targets to train four independent multivariate binary classification pipelines in discerning pwMS versus HC and estimating the level of disease severity, manual dexterity and cognitive capabilities. The final models yielded an AUC-ROC in the hold-out set above 0.7, with the highest performance obtained in estimating the level of fine motor skills (AUC-ROC=0.753). These findings show that keyboard interactions combined with machine learning techniques can be used as an unobtrusive monitoring tool to estimate various levels of clinical disability in pwMS from daily activities and with a high frequency of sampling without increasing patient burden.
多发性硬化症（MS）是一种中枢神经系统的进行性脱髓鞘疾病，其特征是广泛的运动和非运动症状。多发性硬化症患者（pwMS）的残疾程度基于广泛的临床评估，但由于客观和自我报告评估的频率和不准确性，这些评估存在局限性。另一方面，通过设备进行远程健康监测可以提供一种具有成本效益的解决方案，以持续收集更可靠、客观的指标。测量智能手机键盘交互是一种很有前途的工具，因为打字，因此，击键动力学可能会受到 pwMS 可能经历的症状的影响。因此，本文旨在研究通过机器学习技术，从智能手机上收集的键盘交互是否可以深入了解 pwMS 的临床状况。总共有 24 名健康对照（HC）和 102 名 pwMS 被随访了一年。除了通过智能手机交互生成的连续数据外，还收集了临床结果测量值，并将其用作训练四个独立的多元二进制分类管道的目标，以区分 pwMS 与 HC，并估计疾病严重程度、手灵巧度和认知能力的水平。最终模型在保留集上的 AUC-ROC 高于 0.7，在估计精细运动技能水平（AUC-ROC=0.753）方面表现出最高的性能。这些发现表明，结合机器学习技术的键盘交互可以用作一种非侵入性的监测工具，从日常活动中以高频率采样来估计 pwMS 的各种临床残疾程度，而不会增加患者负担。
