| 基于SVM的在线医疗信息服务质量关键影响因素研究 |
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| 引用本文： | 姜明男,薛星群,杨毅.基于SVM的在线医疗信息服务质量关键影响因素研究[J].情报科学,2020,38(3):70-77. |
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| 作者姓名： | 姜明男 薛星群 杨毅 |
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| 作者单位： | 大连理工大学经济管理学院;大连理工大学商学院 |
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| 基金项目： | 教育部人文社会科学基金项目“制度距离对逆向技术溢出效应的影响—基于制度学习的研究”(19YJC790165);辽宁省社科规划基金项目“逆向技术溢出效应对辽宁省产业结构优化的作用及其实现机制研究”(L17CJL005);辽宁省科学技术计划项目“跨境电商平台融资服务模式及动态定价模型研究”(201601054). |
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| 摘 要： | 【目的/意义】通过识别影响在线医疗信息服务质量的敏感因素,为互联网在线医疗信息服务企业实现技术与服务创新提供理论与实践依据,进而提升用户在线医疗信息服务质量。【方法/过程】以问卷调查法与专家访谈法等为基础融合信息增益理论分析各影响因素与在线医疗信息服务质量高低的关联程度,进而以提取的关键影响因素构建在线医疗信息服务质量关键影响因素模型,最后通过SVM构建在线医疗信息服务质量预测模型。【结果/结论】影响在线医疗信息服务质量的20个关键影响因素集中在信息生态的四个重要维度,即信息、信息任、信息技术与信息环境四个维度上,模型88.43%的预测精准度说明SVM对在线医疗信息服务质量具有可靠的预测能力。
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| 关 键 词： | 医疗信息 支持向量机 算法推荐 信息服务质量 信息增益 |
Key Influencing Factors of Online Medical Information Service Quality Based on SVM |
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| JIANG Ming-nan,XUE Xing-qun,YANG Yi.Key Influencing Factors of Online Medical Information Service Quality Based on SVM[J].Information Science,2020,38(3):70-77. |
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| Authors: | JIANG Ming-nan XUE Xing-qun YANG Yi |
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| Affiliation: | (School of Economics and Management,Dalian University of Technology,Dalian 116024,China;Business School,Dalian University of Technology,Panjin 124221,China) |
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| Abstract: | 【Purpose/significance】By identifying the sensitive factors that affect the quality of online medical information service, this paper provides theoretical and practical basis for the realization of technology and service innovation of Internet online medical information service enterprises, so as to improve the quality of online medical information service for users.【Method/process】Based on questionnaire investigation and expert interview method,fusing information gain theory to analyze the influence factors and the connection degree of online medical information service quality, the sensitive influencing factors are extracted and the model for sensitive affecting factors of online medical information service quality is constructed, finally the online medical information service quality prediction model is established through the SVM.【Result/conclusion】The 20 sensitive factors influencing the quality of online medical information service focus on the four important dimensions of information ecology, namely information, information responsibility, information technology and information environment. The prediction accuracy of the model is 88.43%,which indicates that SVM has a reliable prediction ability for the quality of online medical information service. The limitations of the paper is that the limited depth and breadth of the questionnaire, the limited sample size and other reasons may lead to the low prediction accuracy of the model. |
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| Keywords: | medical information support vector machines algorithm recommendation information service quality information gain |
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