Department of Information Technology Management, Tarbiat Modares University, Tehran, Iran.
J Med Syst. 2012 Oct;36(5):2947-58. doi: 10.1007/s10916-011-9773-3. Epub 2011 Sep 13.
Asthma control is a final goal of asthma therapy process. Despite outstanding progress in discovering various variables affecting asthma control levels, disregarding some of them by physicians and variables' inherent uncertainty are the major causes of underestimating of asthma control levels and as a result asthma morbidity and mortality. In this paper, we provide an intelligent fuzzy system as a solution for this problem. Inputs of this system are composed of 14 variables organized in five modules of respiratory symptoms severity, bronchial obstruction, asthma instability, current treatment, and quality of life. Output of this system is degree of asthma control defined in the score (0-10). Evaluation of performance of this system by 42 asthmatic patients at asthma, allergy, immunology research center of Emam Khomeini hospital, Tehran, Iran reinforces that the system's results not only correspond with the evaluations of experienced asthma physicians, but represents slight differences in the levels of asthma control between asthmatic patients.
哮喘控制是哮喘治疗过程的最终目标。尽管在发现影响哮喘控制水平的各种变量方面取得了显著进展，但医生忽略了其中的一些变量，以及变量固有的不确定性，是低估哮喘控制水平的主要原因，从而导致哮喘发病率和死亡率增加。在本文中，我们提供了一个智能模糊系统作为解决此问题的方案。该系统的输入由 14 个变量组成，这些变量组织在五个模块中，分别是呼吸症状严重程度、支气管阻塞、哮喘不稳定、当前治疗和生活质量。该系统的输出是哮喘控制程度的评分（0-10）。在伊朗德黑兰埃马姆霍梅尼医院哮喘、过敏、免疫学研究中心，对 42 名哮喘患者进行的该系统性能评估证实，该系统的结果不仅与经验丰富的哮喘医生的评估相符，而且还显示出哮喘患者之间哮喘控制水平的细微差异。
