Department of Information Technology Management, Tarbiat Modares University, Tehran, Iran.
J Med Syst. 2012 Jun;36(3):1707-17. doi: 10.1007/s10916-010-9631-8. Epub 2010 Dec 3.
Prescription medicine for asthma at primary stages is based on asthma severity level. Despite major progress in discovering various variables affecting asthma severity levels, disregarding some of these variables by physicians, variables' inherent uncertainty, and assigning patients to limited categories of decision making are the major causes of underestimating asthma severity, and as a result low quality of life in asthmatic patients. In this paper, we provide a solution of intelligence fuzzy system for this problem. Inputs of this system are organized in five modules of respiratory symptoms, bronchial obstruction, asthma instability, quality of life, and asthma severity. Output of this system is degree of asthma severity in score (0-10). Evaluating performance of this system by 28 asthmatic patients reinforces that the system's results not only correspond with evaluations of physicians, but represent the slight differences of asthmatic patients placed in specific category introduced by guidelines.
初级阶段哮喘的处方药是基于哮喘严重程度级别。尽管在发现影响哮喘严重程度级别的各种变量方面取得了重大进展，但医生忽略了其中一些变量、变量固有的不确定性，以及将患者分配到有限的决策类别中，这些都是低估哮喘严重程度的主要原因，导致哮喘患者的生活质量下降。在本文中，我们为这个问题提供了一个智能模糊系统的解决方案。该系统的输入分为五个模块，包括呼吸症状、支气管阻塞、哮喘不稳定、生活质量和哮喘严重程度。该系统的输出是哮喘严重程度的分数（0-10）。通过对 28 名哮喘患者进行系统性能评估，结果表明，该系统的结果不仅与医生的评估相符，而且还反映了指南中引入的特定类别中哮喘患者的细微差异。
