基于贝叶斯定理的常见呼吸道传染病分类判别模型研究

A study on Bayesian classification model for common respiratory infectious diseases

目的基于贝叶斯定理建立常见呼吸道传染病的分类判别模型,为传染病暴发疫情调查和实验室检测提供病因线索。方法通过查阅文献、历史疫情数据和暴发疫情调查报告,收集常见传染病的症状、体征、实验室检测结果、流行病学特征及发病数据。基于朴素贝叶斯分类算法原理,采用SAS 9.1.3软件建立分类判别模型,并分别用2013—2015年浙江省发生的流行性感冒、流行性腮腺炎、水痘和麻疹各2起疫情数据对模型的判别效果进行验证。结果 8起疫情的第一位次判别概率最低为20.00%、最高为100.00%、中位数为53.85%,前三位次判别概率最低为55.00%、最高为100.00%、中位数为98.34%。第一位次判别的灵敏度中位数为53.85%,特异度中位数为100.00%,阳性似然比最小为5.73、最大趋向无穷大;前三位次判别的灵敏度中位数为98.34%,特异度中位数为82.14%,阳性似然比最小为1.26、最大趋向无穷大。结论贝叶斯分类判别模型适用于常见呼吸道传染病的分类判别,判别效果达到实际工作要求,能够提高呼吸道传染病暴发疫情病因的早期判别能力。

Objective To provide diagnostic clue for the investigation and laboratory examination in outbreak of common respiratory infectious diseases using a computer - aided classification model. Methods The variables were extracted from medical literature, case data of infectious diseases, reports of outbreaks such as symptoms and signs, abnormal lab test results, epidemiologic features, the incidence rates of the infectious diseases. Then a classification model was constructed using Naive Bayesian classifier and SAS 9. 1. 3 Data from eight historical outbreaks of respiratory infectious diseases were used to test the model. Results Among eight outbreaks, the discriminate probability of diagnosing a disease correctly by ranking it first on the output lists of the model was 53. 85%. The sensitivity was 53. 85% , and specificity was 100. 00% , and ＋ LR was from 5. 73 to 〇〇 . The discriminant probability of diagnosing a disease correctly by ranking it within the three most probable diseases on these lists was 98. 3 4 % . The sensitivity was 98. 34% and the specificity was 82. 14% ,and ＋ LR was from 1. 26 to ∞ . Conclusion A Bayesian classification model could be applied to classification and discriminant of common respiratory infectious diseases, and could improve the ability for early diagnosis of the outbreak caused by respiratory infectious diseases.