Hammad T A, Abdel-Wahab M F, DeClaris N, El-Sahly A, El-Kady N, Strickland G T
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, USA.
Trans R Soc Trop Med Hyg. 1996 Jul-Aug;90(4):372-6. doi: 10.1016/s0035-9203(96)90509-x.
There has been a marked increase in the application of approaches based on artificial intelligence (AI) in the field of computer science and medical diagnosis, but AI is still relatively unused in epidemiological settings. In this study we report results of the application of neural networks (NN; a special category of AI) to schistosomiasis. It was possible to design an NN structure which can process and fit epidemiological data collected from 251 schoolchildren in Egypt using the first year's data to predict second and third years' infection rates. Data collected over 3 years included age, gender, exposure to canal water and agricultural activities, medical history and examination, and stool and urine parasitology. Schistosoma mansoni infection rates were 50%, 78% and 66% at the baseline and the 2 follow-up periods, respectively. NN modelling was based on the standard back-propagation algorithm, in which we built a suitable configuration of the network, using the first year's data, that optimized performance. It was implemented on an IBM compatible computer using commercially available software. The performance of the NN model in the first year compared favourably with logistic regression (NN sensitivity = 83% (95% confidence interval [CI] 78-88%) and positive predictive value (PPV) = 63% (95% CI 57-69%); logistic regression sensitivity = 66% (95% CI 60%-72%) and PPV = 59% (95% CI 53%-65%). The NN model generalized the criteria for predicting infection over time better than logistic regression and showed more stability over time, as it retained its sensitivity and specificity and had better false positive and negative profiles than logistic regression. The potential of NN-based models to analyse and predict wide-scale control programme data, which are inevitably based on unstable egg excretion rates and insensitive laboratory techniques, is promising but still untapped.
基于人工智能（AI）的方法在计算机科学和医学诊断领域的应用显著增加，但AI在流行病学环境中的应用仍然相对较少。在本研究中，我们报告了将神经网络（NN；AI的一个特殊类别）应用于血吸虫病的结果。利用第一年的数据来预测第二和第三年的感染率，设计出一种能够处理和拟合从埃及251名学童收集的流行病学数据的NN结构是可行的。3年收集的数据包括年龄、性别、接触运河水和农业活动、病史和检查，以及粪便和尿液寄生虫学检查。曼氏血吸虫感染率在基线期和2个随访期分别为50%、78%和66%。NN建模基于标准反向传播算法，我们使用第一年的数据构建了一个合适的网络配置，以优化性能。它是在一台IBM兼容计算机上使用商业软件实现的。第一年NN模型的性能优于逻辑回归（NN敏感性 = 83%（95%置信区间[CI]78 - 88%），阳性预测值（PPV） = 63%（95%CI 57 - 69%）；逻辑回归敏感性 = 66%（95%CI 60% - 72%），PPV = 59%（95%CI 53% - 65%）。NN模型比逻辑回归能更好地概括随时间预测感染的标准，并且随时间显示出更高的稳定性，因为它保持了敏感性和特异性，并且与逻辑回归相比具有更好的假阳性和假阴性特征。基于NN的模型分析和预测大规模控制项目数据的潜力是有前景的，但仍未得到充分利用，这些数据不可避免地基于不稳定的虫卵排泄率和不敏感的实验室技术。
