Meta-Analysis and Data Mining-Based Study on the Expression Characteristics of Inflammatory Factors and Causes of Recurrence in Spinal Tuberculosis.

With the rapid development of modern medical information technology, hospitals are accumulating huge amounts of clinical data while providing medical services to patients, and in the era of big data, how to mine valuable information from the huge amount of clinical data so as to make new contributions to future disease diagnosis and medical research. In order to solve this problem, more and more scholars have introduced data mining techniques into the medical field in recent years, and mining and analysing medical data is a hot topic at present. If spinal TB is detected and treated early, not only can spinal deformities be prevented and treated but also the course of treatment can be shortened, the financial burden on the patient can be reduced, spinal function can be maintained, and eradication can be achieved without the need for surgical intervention. Early detection of spinal tuberculosis is the key to preventing and treating it. Therefore, in this paper, we use meta-analysis and data mining techniques to process and analyse the medical data of spinal tuberculosis disease, its main inflammatory factors expression characteristics, and the causes of patient recurrence.