Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran ; Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences-International Campus (TUMS-IC), No #17, 5th Floor, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran.
Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, No #17, 5th Floor, Farredanesh Alley, Ghods St, Enghelab Ave, Tehran, Iran.
Comput Methods Programs Biomed. 2018 Jul;161:145-172. doi: 10.1016/j.cmpb.2018.04.013. Epub 2018 Apr 18.
Diagnosis as the initial step of medical practice, is one of the most important parts of complicated clinical decision making which is usually accompanied with the degree of ambiguity and uncertainty. Since uncertainty is the inseparable nature of medicine, fuzzy logic methods have been used as one of the best methods to decrease this ambiguity. Recently, several kinds of literature have been published related to fuzzy logic methods in a wide range of medical aspects in terms of diagnosis. However, in this context there are a few review articles that have been published which belong to almost ten years ago. Hence, we conducted a systematic review to determine the contribution of utilizing fuzzy logic methods in disease diagnosis in different medical practices.
Eight scientific databases are selected as an appropriate database and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was employed as the basis method for conducting this systematic and meta-analysis review. Regarding the main objective of this research, some inclusion and exclusion criteria were considered to limit our investigation. To achieve a structured meta-analysis, all eligible articles were classified based on authors, publication year, journals or conferences, applied fuzzy methods, main objectives of the research, problems and research gaps, tools utilized to model the fuzzy system, medical disciplines, sample sizes, the inputs and outputs of the system, findings, results and finally the impact of applied fuzzy methods to improve diagnosis. Then, we analyzed the results obtained from these classifications to indicate the effect of fuzzy methods in decreasing the complexity of diagnosis.
Consequently, the result of this study approved the effectiveness of applying different fuzzy methods in diseases diagnosis process, presenting new insights for researchers about what kind of diseases which have been more focused. This will help to determine the diagnostic aspects of medical disciplines that are being neglected.
Overall, this systematic review provides an appropriate platform for further research by identifying the research needs in the domain of disease diagnosis.
诊断作为医疗实践的初始步骤，是复杂临床决策中最重要的部分之一，通常伴随着一定程度的模糊性和不确定性。由于不确定性是医学的固有特性，模糊逻辑方法已被用作降低这种模糊性的最佳方法之一。最近，在诊断的广泛医学领域，已经发表了几篇关于模糊逻辑方法的文献。然而，在这方面，几乎十年前就已经发表了一些综述文章。因此，我们进行了一项系统评价，以确定在不同的医疗实践中利用模糊逻辑方法进行疾病诊断的贡献。
选择了八个科学数据库作为合适的数据库，并采用系统评价和荟萃分析的首选报告项目（PRISMA）方法作为进行本系统和荟萃分析综述的基础方法。鉴于本研究的主要目的，考虑了一些纳入和排除标准，以限制我们的调查。为了进行结构化荟萃分析，根据作者、出版年份、期刊或会议、应用的模糊方法、研究的主要目标、问题和研究空白、用于对模糊系统建模的工具、医学学科、样本量、系统的输入和输出、发现、结果以及最后应用模糊方法对改善诊断的影响，对所有合格文章进行分类。然后，我们分析了从这些分类中获得的结果，以表明模糊方法在降低诊断复杂性方面的效果。
因此，这项研究的结果证实了应用不同模糊方法在疾病诊断过程中的有效性，为研究人员提供了关于哪些疾病受到更多关注的新见解。这将有助于确定医学学科中被忽视的诊断方面。
总的来说，这项系统评价通过确定疾病诊断领域的研究需求，为进一步研究提供了一个合适的平台。
