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云模型-集对分析在糖尿病足溃疡疗效评估中的应用

Application of a Cloud Model-Set Pair Analysis in Efficacy Assessment for Diabetic Ulcers.

作者信息

Kuai Le, Xing Jia-Qi, Zhang Jing-Ting, Xu Xun-Zhe, Wu Min-Feng, Zhao Ke-Qin, Li Bin, Li Fu-Lun

机构信息

Department of Dermatology of Yueyang Hospital, Shanghai University of TCM, Shanghai 200437, China.

Institute of Dermatology, Shanghai Traditional Chinese Medicine Research Institute, Shanghai 201203, China.

出版信息

Evid Based Complement Alternat Med. 2019 Jun 18;2019:8450397. doi: 10.1155/2019/8450397. eCollection 2019.

DOI:10.1155/2019/8450397
PMID:31316577
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6604411/
Abstract

Because treatment of diabetic ulcers includes various uncertainties, efficacy assessments are needed and significant. In previous studies, set pair analysis (SPA) has been applied to the efficacy assessments of traditional Chinese medicine (TCM) that pick out uncertainties related to the development and prognosis of disease. Optimized clinical protocols of SPA improve clinical efficacy. In the article, cloud model (CM) is employed to improve SPA, and a novel efficacy assessment method for a treatment of diabetic ulcers is proposed based on the cloud model-set pair analysis (CM-SPA). It is recommended to replace connection degree (CD) with cloud connection degree (CCD) that the efficacy assessment results are shown as normal clouds. Then, three diabetic ulcers patients treated with TCM made importance assessment by both CM-SPA and AHP based SPA. The comparison of assessment results shows that the CM-SPA is efficacious for the efficacy assessment of a treatment for diabetic ulcers and the results will be more scientific and accurate via CM-SPA.

摘要

由于糖尿病溃疡的治疗存在各种不确定性,疗效评估是必要且重要的。在以往的研究中,集对分析(SPA)已应用于中医(TCM)的疗效评估,该方法能够找出与疾病发生发展及预后相关的不确定性因素。优化后的SPA临床方案可提高临床疗效。本文采用云模型(CM)对SPA进行改进,提出了一种基于云模型-集对分析(CM-SPA)的糖尿病溃疡治疗疗效评估新方法。建议用云联系度(CCD)取代联系度(CD),使疗效评估结果以正态云的形式呈现。然后,对3例接受中医治疗的糖尿病溃疡患者分别采用CM-SPA和基于层次分析法(AHP)的SPA进行重要性评估。评估结果对比表明,CM-SPA对糖尿病溃疡治疗疗效评估有效,且通过CM-SPA得出的结果将更加科学准确。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/a50018bee3db/ECAM2019-8450397.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/52f41feea0e9/ECAM2019-8450397.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/05860a383e72/ECAM2019-8450397.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/51010cd0c17d/ECAM2019-8450397.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/a50018bee3db/ECAM2019-8450397.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/52f41feea0e9/ECAM2019-8450397.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/05860a383e72/ECAM2019-8450397.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/51010cd0c17d/ECAM2019-8450397.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e5a/6604411/a50018bee3db/ECAM2019-8450397.004.jpg

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本文引用的文献

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PLoS One. 2018 Mar 21;13(3):e0193576. doi: 10.1371/journal.pone.0193576. eCollection 2018.
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IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045.
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Evid Based Complement Alternat Med. 2020 Jun 16;2020:5091671. doi: 10.1155/2020/5091671. eCollection 2020.
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