Predicting active pulmonary tuberculosis using an artificial neural network
Predicting active pulmonary tuberculosis using an artificial neural network
复制标题
DOI：
10.1378/chest.116.4.968
复制
发表时间：
1999-10-01
期刊：
影响因子：
9.6
通讯作者：
Grant, BJB
中科院分区：
文献类型：
--
作者：
El-Solh, AA;Hsiao, CB;Grant, BJB
背景资料：结核病（TB）的医院暴发已归因于未被识别的肺TB，在识别活动性TB的索引病例中的准确评估在防止疾病的transmissions.Objectives中是必不可少的：为了开发一种人工神经网络，其使用临床和放射学信息来预测活动性肺TB在呈现在上级医生意见的卫生保健设施时，设计：非同期前瞻性研究，地点：大学附属医院。参与者：563例隔离事件的推导组和119例隔离事件的验证组。干预措施：一般回归神经网络（GRNN）被用来开发predictive model.Measurements：神经网络的预测精度与临床医生的评估相比，结果：预测精度进行评估的c指数，这是相当于下的面积的接收器工作特征曲线。GRNN显著优于医生的预测，计算的c指数（+/- SEM）分别为0.947 +/- 0.028和0.61 +/- 0.045，（p < 0.001），当GRNN应用于验证组时，相应的c指数分别为0.923 +/- 0.058和0.716 +/- 0.095。人工神经网络可以比医生的临床评估更准确地识别活动性肺结核患者。
Background: Nosocomial outbreaks of tuberculosis (TB) have been attributed to unrecognized pulmonary TB, Accurate assessment in identifying index cases of active TB is essential in preventing transmission of the disease.Objectives: To develop an artificial neural network using clinical and radiographic information to predict active pulmonary TB at the time of presentation at a health-care facility that is superior to physicians' opinion,Design: Nonconcurrent prospective study,Setting: University-affiliated hospital.Participants: A derivation group of 563 isolation episodes and a validation group of 119 isolation episodes. Interventions: A general regression neural network (GRNN) was used to develop the predictive model.Measurements: Predictive accuracy of the neural network compared with clinicians' assessment,Results: Predictive accuracy was assessed by the c-index, which is equivalent to the area under the receiver operating characteristic curve. The GRNN significantly outperformed the physicians' prediction, with calculated c-indices (+/- SEM) of 0.947 +/- 0.028 and 0.61 +/- 0.045, respectively (p < 0.001), When the GRNN was applied to the validation group, the corresponding c-indices were 0.923 +/- 0.058 and 0.716 +/- 0.095, respectively.Conclusion: An artificial neural network can identify patients with active pulmonary TB more accurately than physicians' clinical assessment.