@Article{info:doi/10.2196/17580，作者=“Hosseini, Seyed Ahmad and Jamshidnezhad, Amir and Zilaee, Marzie and Fouladi Dehaghi, Behzad and Mohammadi, Abbas and Hosseini, Seyed Mohsen”，标题=“基于神经网络的临床预测系统识别藏红花(Crocus sativus L)补充治疗过敏性哮喘的临床效果:模型评估研究”，期刊=“JMIR Med Inform”，年=“2020”，月=“7”，日=“6”，卷=“8”，数=“7”，页=“e17580”，关键词=“Asthma”;机器学习;临床预测系统;神经网络;补充疗法;番红花;藏红花L"，摘要="背景:哮喘通常与慢性气道炎症有关，是每年超过一百万人死亡的潜在原因。藏红花(Crocus sativus L)，通常被称为藏红花，当以传统药物的形式使用时，已被证明具有抗炎作用，可能对哮喘患者有益。目的:本研究的目的是建立一种应用人工神经网络的临床预测系统来检测芫花L补充剂对过敏性哮喘患者的影响。方法:利用从哮喘患者的临床、免疫、血液学和人口统计信息中提取的特征，开发了一种遗传算法改进的神经网络预测系统来检测C sativus L的有效性水平。 The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. Results: The best overall performance of the clinical prediction system was an accuracy greater than 99{\%} for training and testing data. The genetic algorithm--modified neural network predicted the level of effect with high accuracy for anti--heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25{\%}-75{\%}) for testing data (anti-HSP: 96.5{\%}; hs-CRP: 98.9{\%}; FEV1: 98.1{\%}; FVC: 97.5{\%}; FEV1/FVC ratio: 97{\%}; and FEF25{\%}-75{\%}: 96.7{\%}, respectively). Conclusions: The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma. ", issn="2291-9694", doi="10.2196/17580", url="https://medinform.www.mybigtv.com/2020/7/e17580", url="https://doi.org/10.2196/17580", url="http://www.ncbi.nlm.nih.gov/pubmed/32628613" }