Lones Michael A, Alty Jane E, Cosgrove Jeremy, Duggan-Carter Philippa, Jamieson Stuart, Naylor Rebecca F, Turner Andrew J, Smith Stephen L
School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK.
Department of Neurology, Leeds General Infirmary, Leeds, UK.
J Med Syst. 2017 Sep 25;41(11):176. doi: 10.1007/s10916-017-0811-7.
Parkinson's disease (PD) is a neurodegenerative movement disorder. Although there is no cure, symptomatic treatments are available and can significantly improve quality of life. The motor, or movement, features of PD are caused by reduced production of the neurotransmitter dopamine. Dopamine deficiency is most often treated using dopamine replacement therapy. However, this therapy can itself lead to further motor abnormalities referred to as dyskinesia. Dyskinesia consists of involuntary jerking movements and muscle spasms, which can often be violent. To minimise dyskinesia, it is necessary to accurately titrate the amount of medication given and monitor a patient's movements. In this paper, we describe a new home monitoring device that allows dyskinesia to be measured as a patient goes about their daily activities, providing information that can assist clinicians when making changes to medication regimens. The device uses a predictive model of dyskinesia that was trained by an evolutionary algorithm, and achieves AUC>0.9 when discriminating clinically significant dyskinesia.
帕金森病（PD）是一种神经退行性运动障碍。虽然无法治愈，但有对症治疗方法，可显著提高生活质量。PD的运动特征是由神经递质多巴胺分泌减少引起的。多巴胺缺乏症最常采用多巴胺替代疗法治疗。然而，这种疗法本身可能导致进一步的运动异常，即运动障碍。运动障碍包括不自主的抽搐运动和肌肉痉挛，通常可能很剧烈。为了尽量减少运动障碍，必须精确调整给药量并监测患者的运动情况。在本文中，我们描述了一种新型家庭监测设备，该设备可以在患者进行日常活动时测量运动障碍，提供有助于临床医生调整药物治疗方案的信息。该设备使用由进化算法训练的运动障碍预测模型，在区分具有临床意义的运动障碍时，曲线下面积（AUC）>0.9。
