TY - JOUR AU - Hosseini, Seyed Ahmad AU - Jamshidnezhad, Amir AU - Zilaee, Marzie AU - Fouladi Dehaghi, Behzad AU - Mohammadi, Abbas AU - Hosseini, Seyed Mohsen PY - 2020 DA - 2020/7/6 TI -基于神经网络的藏红花补充治疗过敏性哮喘临床疗效预测系统研究模型评估研究JO - JMIR Med Inform SP - e17580 VL - 8 IS - 7 KW -哮喘KW -机器学习KW -临床预测系统KW -神经网络KW -补充疗法KW -藏红花KW -藏红花L AB -背景:哮喘通常与慢性气道炎症相关，是每年超过100万人死亡的潜在原因。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:利用哮喘患者的临床、免疫、血液学和人口统计学信息提取特征，建立遗传算法改进的神经网络预测系统，检测C sativus L的疗效水平。该研究包括来自18至65岁轻度或中度过敏性哮喘患者的男性(n=40)和女性(n=40)的数据。该模型的目的是估计和预测C sativus L补充剂对每个哮喘危险因素的影响水平，并预测哮喘患者的缓解水平。采用遗传算法提取临床预测系统的输入特征，提高临床预测系统的预测性能。此外，建立了人工神经网络组件的优化模型，用于对哮喘患者进行C sativus L补充治疗。结果:临床预测系统的最佳综合性能是训练和测试数据的准确率大于99%。 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. SN - 2291-9694 UR - https://medinform.www.mybigtv.com/2020/7/e17580 UR - https://doi.org/10.2196/17580 UR - http://www.ncbi.nlm.nih.gov/pubmed/32628613 DO - 10.2196/17580 ID - info:doi/10.2196/17580 ER -