论文标题
基于机器学习的严重性预测工具，用于使用密歇根州神经病筛查仪器的糖尿病感觉多发性病变
A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations
论文作者
论文摘要
背景：糖尿病感官多神经病（DSPN）是与疼痛神经病，足部溃疡和截肢相关的糖尿病患者的主要长期并发症。密歇根神经病筛查仪器（MNSI）是DSPN最常见的筛选技术之一，但是，它不提供任何直接的严重性分级系统。方法：用于设计和建模用于MNSI的DSPN严重程度分级系统，使用了19年的糖尿病干预措施和并发症（EDIC）临床试验的数据。使用机器学习工具研究了MNSI变量和患者结果，以识别DSPN识别中具有较高关联的功能。生成了基于多变量逻辑回归的列表，并验证了DSPN严重性分级的验证。结果：MNSI的前7个排名特征：10 gm细丝，振动感（R），振动感知（L），先前的糖尿病神经病，畸形，愈伤组织的外观和裂变的出现被确定为使用多余树型识别DSPN的关键特征。内部和外部数据集的曲线曲线（AUC）下的面积分别为0.9421和0.946。从开发的nom图中，可以预测具有DSPN的概率，并根据概率得分开发了MNSI的DSPN严重性评分系统。模型性能在独立数据集上进行了验证。将患者分为四个严重程度：使用10.5、12.7和15的DSPN概率分别不到50％，75％至90％和90％以上的DSPN概率，缺乏，轻度，中度和重度。结论：这项研究提供了一种简单，易于使用且可靠的算法，用于定义DSPN患者的预后和管理。
Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulceration and amputation. The Michigan neuropathy screening instrument (MNSI) is one of the most common screening techniques for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modelling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. MNSI variables and patient outcomes were investigated using machine learning tools to identify the features having higher association in DSPN identification. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading. Results: The top-7 ranked features from MNSI: 10-gm filament, Vibration perception (R), Vibration perception (L), previous diabetic neuropathy, the appearance of deformities, appearance of callus and appearance of fissure were identified as key features for identifying DSPN using the extra tree model. The area under the curve (AUC) of the nomogram for the internal and external datasets were 0.9421 and 0.946, respectively. From the developed nomogram, the probability of having DSPN was predicted and a DSPN severity scoring system for MNSI was developed from the probability score. The model performance was validated on an independent dataset. Patients were stratified into four severity levels: absent, mild, moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPN probability less than 50%, 75% to 90%, and above 90%, respectively. Conclusions: This study provides a simple, easy-to-use and reliable algorithm for defining the prognosis and management of patients with DSPN.
