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首页> 外文期刊>JMIR Medical Informatics >Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor Analysis From a National Survey
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Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor Analysis From a National Survey

机译：用健康营养考试的干眼疾病解释模型：从国家调查中的机器学习和基于网络的因子分析

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Background Dry eye disease (DED) is a complex disease of the ocular surface, and its associated factors are important for understanding and effectively treating DED. Objective This study aimed to provide an integrative and personalized model of DED by making an explanatory model of DED using as many factors as possible from the Korea National Health and Nutrition Examination Survey (KNHANES) data. Methods Using KNHANES data for 2012 (4391 sample cases), a point-based scoring system was created for ranking factors associated with DED and assessing patient-specific DED risk. First, decision trees and lasso were used to classify continuous factors and to select important factors, respectively. Next, a survey-weighted multiple logistic regression was trained using these factors, and points were assigned using the regression coefficients. Finally, network graphs of partial correlations between factors were utilized to study the interrelatedness of DED-associated factors. Results The point-based model achieved an area under the curve of 0.70 (95% CI 0.61-0.78), and 13 of 78 factors considered were chosen. Important factors included sex (+9 points for women), corneal refractive surgery (+9 points), current depression (+7 points), cataract surgery (+7 points), stress (+6 points), age (54-66 years; +4 points), rhinitis (+4 points), lipid-lowering medication (+4 points), and intake of omega-3 (0.43%-0.65% kcal/day; ?4 points). Among these, the age group 54 to 66 years had high centrality in the network, whereas omega-3 had low centrality. Conclusions Integrative understanding of DED was possible using the machine learning–based model and network-based factor analysis. This method for finding important risk factors and identifying patient-specific risk could be applied to other multifactorial diseases.
机译：背景技术干眼疾病（DED）是眼表面的复杂疾病，其相关因素对于理解和有效治疗李众不可。目的本研究旨在通过从韩国国家健康和营养考试调查（KNHANES）数据中使用尽可能多的因素，提供一种综合和个性化模型。使用knhanes数据进行2012年的方法（4391个样本案例），为与DED和评估患者特定的常规风险相关的排名因素来创建基于点的评分系统。首先，决定树木和套索用于分别持续因素并分别选择重要因素。接下来，使用这些因素训练测量加权的多个逻辑回归，并且使用回归系数分配点。最后，利用因子之间部分相关性的网络图来研究虚线相关因素的相互关联性。结果基于点的模型在0.70（95％CI 0.61-0.78）的曲线下实现了一个面积，选择了78个因素中的13个。重要因素包括性别（妇女+9点），角膜屈光外科（+9分），目前抑郁症（+7点），白内障手术（+7分），压力（+6点），年龄（54-66岁） ; + 4点），鼻炎（+ 4点），降脂药物（+ 4分），并摄入ω-3（0.43％-0.65％Kcal / Day;？4分）。其中，年龄组54至66岁在网络中具有很高的中心，而欧米茄3的中心性低。结论使用基于机器学习的模型和基于网络的因子分析，可以实现对DED的一体化了解。该方法可以应用于寻找重要风险因素并识别患者特定风险的方法。可以应用于其他多因素疾病。

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