通过应用机器学习方法,利用年龄身高Z值(HAZ)预测先天性表皮松解症患者的蛋白质-能量营养不良情况

O. S. Orlova
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The study involved 101 patients aged 3 to 18 years with simplex (n=25), junctional (n=10), and dystrophic (n=66) СEB. The Birmingham EB Severity Score, laboratory and anthropometric parameters, as well as data on the presence of gastrointestinal complications, were used for the analysis of disease progression and predictive model construction. The Scikit-learn library of the programming language Python was utilized for building the machine learning model.Results. In the construction of the predictive model, the RandomForestClassifier model showed the best results. The developed machine learning model can correctly determine whether a patient has chronic protein-energy malnutrition (class 1, HAZ < –2) or not (class 0, HAZ > –2) with an accuracy of 92%, sensitivity of 85.7%, and specificity of 100%.Conclusions. 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摘要

先天性大疱性表皮松解症(СЕВ)是一组遗传和临床异质性疾病,其特点是在皮肤和粘膜上易形成水疱和/或糜烂,创伤极小。营养缺乏是 EB 最常见的并发症,其发病受到多种因素的影响。构建各种形式 EB 患者发生蛋白质-能量营养不良的预测模型,确定影响预测模型灵敏度的主要特征,并根据该类患者出现蛋白质-能量营养不良的回顾性数据评估模型的有效性。研究涉及 101 名 3 至 18 岁的单纯性(25 人)、交界性(10 人)和营养不良性(66 人)СEB 患者。伯明翰 EB 严重程度评分、实验室和人体测量参数以及胃肠道并发症数据被用于分析疾病进展和构建预测模型。在构建机器学习模型时,使用了编程语言 Python 的 Scikit-learn 库。在构建预测模型的过程中,随机森林分类器(RandomForestClassifier)模型的效果最佳。所开发的机器学习模型可以正确判断患者是否患有慢性蛋白质能量营养不良(1级,HAZ<-2)(0级,HAZ>-2),准确率为92%,灵敏度为85.7%,特异性为100%。本研究提出的机器学习模型可预测年龄身高 Z 值(HAZ),在医疗实践和临床研究中具有实际意义。该模型可用于 EB 患者蛋白质能量营养不良的早期诊断,使医护人员能够及时开始营养支持,预防疾病可能出现的并发症,并为患者制定个性化的营养和治疗方案。
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Using the Height-for-Age Z-score (HAZ) to predict protein-energy malnutrition in patients with congenital epidermolysis bullosa through the application of machine learning methods
Congenital epidermolysis bullosa (СЕВ) is a group of genetically and clinically heterogeneous diseases characterized by a tendency to form blisters and/or erosions on the skin and mucous membranes with minimal trauma. Nutritional deficiency stands as the most common complication observed in EB, with its development influenced by a multitude of contributing factors.Purpose of the study. To construct a predictive model for the development of protein-energy malnutrition in patients with various forms of EB, identify the main features affecting the sensitivity of the predictive model, and evaluate the model’s validity based on retrospective data on the presence of protein-energy malnutrition in this patient category.Methods. The study involved 101 patients aged 3 to 18 years with simplex (n=25), junctional (n=10), and dystrophic (n=66) СEB. The Birmingham EB Severity Score, laboratory and anthropometric parameters, as well as data on the presence of gastrointestinal complications, were used for the analysis of disease progression and predictive model construction. The Scikit-learn library of the programming language Python was utilized for building the machine learning model.Results. In the construction of the predictive model, the RandomForestClassifier model showed the best results. The developed machine learning model can correctly determine whether a patient has chronic protein-energy malnutrition (class 1, HAZ < –2) or not (class 0, HAZ > –2) with an accuracy of 92%, sensitivity of 85.7%, and specificity of 100%.Conclusions. The machine learning model presented in this study predicts the values of the Height-for-Age Z-score (HAZ) and can have practical significance in medical practice and clinical research. The model can be used for early diagnosis of protein-energy malnutrition in patients with EB, which may allow healthcare professionals to timely start nutritional support and prevent possible complications of the disease, as well as develop individual nutrition and treatment plans for patients.
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