|
|
Research on prediction model of ethnic minority health service utilization in Guizhou Province based on artificial neural network |
1.School of Medcine and Health Management,Guizhou Medical University,Guizhou Province,Guiyang 550025,China;
2.Department of Science and Technology,Guizhou Medical University,Guizhou Province,Guiyang 550025,China |
|
|
Abstract Objective To study the influence of the health literacy on health service utilization among ethnic minorities in Guizhou Province by a method of constructing artificial neural network model,making the prediction of health service utilization intelligent.Methods From June to December 2019,1031 resident ethnic minorities aged 15 to 69 years old in Guizhou Province were selected by multistage sampling for a questionnaire survey.Univariate analysis was used to screen the variables,and multivariate analysis was performed by an artificial neural network model.Results Among the 1031 minority samples,the outpatient service utilization rate was 7.08%,and the inpatient service utilization rate was 8.44%.There were no statistically significant differences in different individual basic information,total score of health literacy and scores of each category between outpatient utilization group and outpatient non-utilization group(P>0.05).There were statistically significant differences in the occupation,the total score of health literacy,and scores of health information literacy and safety and first aid literacy between the inpatient non-utilization group and the inpatient utilization group,the differences were statistically significant(P<0.05).Then occupation(P=0.025),health information literacy(P=0.025),safety and first aid literacy(P=0.000)were selected as input layer nodes,and an artificial neural network inpatient service utilization prediction model was constructed.Based on the model,155 sets of data were used for verification.All the absolute value of errors did not exceed 1,with the maximum near 0,so the model fitting effect was good.Conclusion Occupation,health information literacy,safety and first aid literacy significantly affect the utilization of inpatient service for ethnic minorities in Guizhou Province.The artificial neural network model is highly accurate and can be used for further prediction of health service utilization.
|
|
|
|
|
[6] |
李玉翠,周正,彭漪,等.基于机器学习的东湖富营养化研究[J].人民长江,2018,49(17):12-17.
|
[7] |
杜栋,庞庆华,吴炎.现代综合评价方法与案例精选[M].北京:清华大学出版社,2018:102
|
[8] |
吴晖,韩海庭,屈秀伟,等.大数据征信算法的可解释性研究[J].征信,2020,38(5):44-51.
|
[9] |
刘志强,王玲,贾海江,等.基于BP 神经网络的驾驶员制动行为模型研究[J].机械设计与制造,2019,(6):37-41.
|
[10] |
朱瑜馨,张锦宗,赵军.基于人工神经网络的森林资源预测模型研究[J].干旱区资源与环境,2005,19(1):101-104.
|
[11] |
陈玉玲,吴保国,崔岩,等.基于BP 神经网络的华北落叶松小班蓄积预估模型研究与应用[J].中国农业科技导报,2019,21(7):82-93.
|
[12] |
周谦豪,姚占雷,许鑫.图书馆健康信息素养教育的调研与分析[J].图书馆学研究,2020,(10):77-86.
|
[13] |
戴明艳,张存辉,忻振慧,等.张家口市医学生安全与急救素养水平及影响因素分析[J].中国健康教育,2018,34(3):268-271.
|
[14] |
王虎峰.全球健康促进30年的共识与经验——基于全球健康促进大会宣言的文本分析[J].中国行政管理,2019,(12):133-139.
|
[15] |
雷明昊.发展型自治——中国民族区域自治的特色与优势[J].广西民族研究,2018,(2):49-58.
|
[1] |
赵黎.新医改与中国农村医疗卫生事业的发展——十年经验、现实困境及善治推动[J].中国农村经济,2019,(9):48-69.
|
[2] |
单诗洋.2014年辽宁省居民健康素养调查分析[D].长春:吉林大学,2017.
|
[3] |
金帅岐,李贺,沈旺,等.用户健康信息搜寻行为的影响因素研究——基于社会认知理论三元交互模型[J].情报科学,2020,38(6):53-61,75.
|
[4] |
中国健康教育中心.中国居民健康素养监测报告[M].北京:人民卫生出版社,2018:36-42.
|
[5] |
贾绪计,王庆瑾,李雅倩,等.健康素养的内涵与评价[J].北京师范大学学报:社会科学版(社会科学版),2019,(2):66-72.
|
|
|
|