Department of Respiratory and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, Hubei Province, China.
J Healthc Eng. 2021 Nov 1;2021:9018408. doi: 10.1155/2021/9018408. eCollection 2021.
In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.
在中医中，哮喘病例包含大量经验数据，这些数据是医生全年临床诊断的结果。数据关联分析方法是挖掘（1）处方和开方者（在这种情况下是医生）与（2）特定疾病在医院中的症状和药物之间关联的常用机制之一。在本文中，首先对 Apriori 算法在挖掘医疗案例数据方面的预期性能和缺点进行了全面分析。其次，我们提出了一种传统 Apriori 算法的扩展版本，该算法主要基于计算机对位串逻辑运算的快速响应。提出的和现有的 Apriori 算法在运行时间、频繁项集挖掘和强关联规则方面进行了比较评估。实验和模拟结果均表明，在针对哮喘药物和联合症状-药物数据进行关联分析时，所提出的扩展 Apriori 算法在应用于哮喘病例数据时表现优于现有算法。此外，通过实验数据和观察，验证了心哮喘病例数据与药物之间的关联关系在分析哮喘病例数据方面是有效的，具有重要的应用价值。
