Abstract
Antimicrobial resistance (AMR) in bacteria is one of the most critical public health challenges humanity is facing. In our country, infections caused by AMR bacteria, such as multidrug-resistant Gram-negative rods and non-tuberculous mycobacteria, are spreading without effective treatment options, leading to the increasing complexity of bacterial infections. Given this context, there is a strong need to establish efficient treatment strategies for AMR bacterial infections. Understanding the impact of patient background factors and disease pathophysiology on treatment outcomes is crucial for advancing personalized medicine in infectious diseases and selecting more effective treatment options. However, this aspect remains insufficiently elucidated at present. In recent years, medical big data analysis has made it possible to conduct clinical evaluations of drug treatments that reflect real-world clinical settings, encompassing diverse patient populations, and various factors, and such analyses have commenced across various diseases. We are conducting research to comprehensively explore factors and drug interactions that influence the efficacy of infectious disease treatments using medical big data, which encompasses the diversity and complexity of diseases. To establish a system for conducting rapid clinical research during future outbreaks of emerging infectious diseases, it is desirable to establish an analysis platform utilizing big medical data.
