基于机器学习技术新冠疫情下中国北方地区医生情绪化进食行为预测模型构建

王起赫; 刘飒娜; 方海琴; 梁栋; 张雪楠; 高会; 李金良; 刘爱东

doi:10.11847/zgggws1140655

基于机器学习技术新冠疫情下中国北方地区医生情绪化进食行为预测模型构建

doi: 10.11847/zgggws1140655

1.
国家食品安全风险评估中心，北京 100022
2.
中国疾病预防控制中心环境与健康相关产品安全所
3.
中国医学科学院
4.
哈尔滨市第六医院

详细信息

作者简介:
王起赫（1988 – ），男，黑龙江哈尔滨人，助理研究员，博士，研究方向：国家食品安全标准制修订、国民营养计划、营养科普宣教

通讯作者:
刘爱东，E-mail：liuaidong@cfsa.net.cn

中图分类号: R 395.6
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- 被引次数: 0
出版历程
- 接收日期: 2022-10-21
- 网络出版日期: 2023-03-31
- 刊出日期: 2023-04-10

Construction of a machine learning-based prediction model for emotional eating during COVID-19 pandemic among doctors in North China

1.
China National Center for Food Safety Risk Assessment, Beijing 100022, China

摘要

摘要: 目的构建新型冠状病毒感染疫情（简称新冠疫情）下中国北方地区医生情绪化进食行为预测模型，为改善医生健康膳食模式提供科学依据。方法于2022年5 — 8月采用随机抽样方法在中国北方地区黑龙江、辽宁、吉林、河北、山东、山西、北京、天津8个省（直辖市）抽取39家新冠疫情定点收治医院共2 316名医生进行一般情况调查表、工作家庭冲突量表、大五人格量表及情绪化进食量表调查，基于机器学习技术建立深度神经网络模型预测新冠疫情下医生情绪化进食行为的因素。结果 2094名医生有效完成问卷调查，情绪化进食行为总分为（51.48 ± 17.37）分，其中愤怒情绪进食、焦虑情绪进食、抑郁情绪进食和积极情绪进食得分为（11.31 ± 4.07）、（16.72 ± 7.56）、（11.02 ± 3.24）和（12.43 ± 4.27）分；基于机器学习技术构建了一个结构为21 – 19 – 14 – 9 – 1的医生情绪化进食行为预测的深度神经网络模型，该模型的R²、MAE、MSE和RMSE值分别为0.926、0.039、0.003和0.056；此模型预测结果显示，新冠疫情下中国北方地区医生情绪化进食行为的前5位重要因素依次为饮酒、工作家庭冲突、营养不均衡、性别（男）和饮食不规律。结论新冠疫情下中国北方地区医生情绪化进食情况不容乐视，应用机器学习技术能够有效精确预测医生的情绪化进食风险，不良饮食习惯和工作家庭冲突是导致医生情绪化进食行为的主要因素。
- 情绪化进食行为 /
- 预测模型 /
- 构建 /
- 机器学习技术 /
- 医生 /
- 北方地区 /
- 新型冠状病毒感染疫情
Abstract: Objective To construct a prediction model for emotional eating behavior during coronavirus disease 2019 (COVID-19) pandemic among doctors in northern region of China for providing evidence to the promotion of healthy dietary patterns in the doctors. Methods An on-site self-administered questionnaire survey was conducted among 2 316 doctors randomly recruited at 39 COVID-19 designated hospitals in 8 provincial-level administrative divions in northern China during May – August 2022. Relevant information of the doctors were collected with a general questionnaire, work-family conflict scale, NEO Five-Factor Inventory and emotional eating scale. Deep neural network (DNN) was used to develop a prediction model for associates of emotional eating during the COVID-19 pandemic in the doctors. Results For 2 094 participants with complete information, the mean overall score of emotional eating during the COVID-19 pandemic was 51.48 ± 17.37 and the dimensional scores were 11.31 ± 4.07 for anger influenced eating, 16.72 ± 7.56 for anxiety influenced eating, 11.02 ± 3.24 for depression influenced eating, and 12.43 ± 4.27 for positive emotion influenced eating. A DNN model with 21-19-14-9-1 network framework was constructed for predicting emotional eating behaviors in the doctors during the COVID-19 pandemic and the parameters of the model were 0.926 for R², 0.039 for mean absolute error, 0.003 for mean squared error, and 0.056 for root mean squared error, respectively. Based on the modeling results, the top five predictors for emotional eating model were alcohol consumption, work-family conflict, unbalanced nutrition, male gender, and irregular dietary pattern. Conclusion Emotional eating behaviors during the COVID-19 pandemic was not rare and mainly influenced by unhealthy eating habits and work-family conflict among doctors in northern China. Machine learning could be used to predict effectively and accurately the risk of emotional eating behavior for the doctors.
- emotional eating behavior /
- prediction model /
- construction /
- doctors /
- machine-learning /
- northern region /
- COVID-19 epidemic

HTML全文

图 1 新冠疫情下中国北方地区医生情绪化进食行为的深度神经网络模型

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图 2 新冠疫情下中国北方地区医生情绪化进食行为影响因素的相对重要性

下载: 全尺寸图片幻灯片

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