基于机器学习的糖尿病感觉运动性多发性神经病严重程度预测工具，使用密歇根神经病筛查仪器
A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations
摘要：
工作背景：糖尿病感觉运动性多发性神经病（DSPN）是糖尿病患者的主要长期并发症，与疼痛性神经病、足部溃疡和截肢相关。密歇根神经病筛查工具（MNSI）是DSPN最常见的筛查技术之一，但它没有提供任何直接的严重程度分级系统。方法：为了设计和模拟MNSI的DSPN严重程度分级系统，使用了来自糖尿病干预和并发症流行病学（EDIC）临床试验的19年数据。使用机器学习工具研究MNSI变量和患者结局，以识别在DSPN识别中具有较高关联性的特征。生成并验证了基于多变量逻辑回归的诺模图用于DSPN严重程度分级。结果如下：来自MNSI的前7个排名特征：10-gm细丝、振动感知（R）、振动感知（L）、先前的糖尿病神经病变、畸形的出现、骨痂的出现和裂缝的出现被确定为使用额外树模型识别DSPN的关键特征。内部和外部数据集列线图的曲线下面积（AUC）分别为0.9421和0.946。从开发的诺模图，有DSPN的概率进行了预测和MNSI的DSPN严重程度评分系统的概率得分开发。在独立数据集上验证了模型性能。将患者分为4个严重程度水平：无、轻度、中度和重度，DSPN概率分别小于50%、75%-90%和90%以上的截止值为10.5、12.7和15。结论：本研究提供了一个简单，易于使用和可靠的算法来确定DSPN患者的预后和管理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
