Institute of Clinical Pharmacology, Goethe-University, Theodor Stern Kai 7, 60590, Frankfurt am Main, Germany.
Fraunhofer Institute of Molecular Biology and Applied Ecology-Project Group Translational Medicine and Pharmacology (IME-TMP), Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany.
J Neurol. 2020 Feb;267(2):469-478. doi: 10.1007/s00415-019-09604-6. Epub 2019 Nov 1.
Chemosensory loss is a symptom of Parkinson's disease starting already at preclinical stages. Their appearance without an identifiable etiology therefore indicates a possible early symptom of Parkinson's disease. Supervised machine-learning was used to identify parameters that predict Parkinson's disease among patients having sought medical advice for chemosensory symptoms.
Olfactory, gustatory and demographic parameters were analyzed in 247 patients who had reported for chemosensory symptoms. Unsupervised machine-learning, implanted as so-called fast and frugal decision trees, was applied to map these parameters to a diagnosis of Parkinson's disease queried for in median 9 years after the first interview.
A symbolic hierarchical decision rule-based classifier was created that comprised d = 5 parameters, including scores in tests of odor discrimination, odor identification and olfactory thresholds, the age at which the chemosensory loss has been noticed, and a familial history of Parkinson's disease. The rule set provided a cross-validated negative predictive performance of Parkinson's disease of 94.1%; however, its balanced accuracy to predict the disease was only 58.9% while robustly above guessing.
Applying machine-learning techniques, a classifier was developed that took the shape of a set of six hierarchical rules with binary decisions about olfaction-related features or a familial burden of Parkinson's disease. Its main clinical strength lies in the exclusion of the possibility of developing Parkinson's disease in a patient with olfactory or gustatory loss.
化学感觉丧失是帕金森病的一个症状，早在临床前阶段就已经出现。因此，没有明确病因的出现表明可能是帕金森病的早期症状。本研究采用有监督机器学习的方法，旨在识别出因化学感觉症状而寻求医疗建议的患者中可能预示帕金森病的参数。
对 247 名出现化学感觉症状的患者进行了嗅觉、味觉和人口统计学参数分析。将无监督机器学习，即所谓的快速而节俭决策树，应用于将这些参数映射到中位数为首次就诊后 9 年的帕金森病诊断。
创建了一个基于符号层次决策规则的分类器，该分类器包含 5 个参数，包括气味辨别、气味识别和嗅觉阈值测试的分数、化学感觉丧失被注意到的年龄，以及帕金森病的家族史。该规则集提供了帕金森病的交叉验证阴性预测性能为 94.1%；然而，其预测疾病的平衡准确性仅为 58.9%，但稳健性高于猜测。
应用机器学习技术，开发了一种分类器，其形状为一组具有二进制决策的六个层次规则，用于与嗅觉相关特征或帕金森病家族负担相关的特征。其主要临床优势在于排除了嗅觉或味觉丧失的患者发生帕金森病的可能性。
