Research on Basic Clinical Treatment Pattern Mining Based on Electronic Medical Record Big Data

This paper analyzed the basic clinical treatment pattern by exploring the relationship between diseases, symptoms and drugs, which help non-medical people understand the basic clinical treatment pattern, so as to better carry out medical and health big data mining and eliminate public prejudice against symptomatic treatment. The FP growth algorithm was used to mine the association rules from EMR big data. Combined with intersection analysis, the basic clinical treatment pattern was summarized. 507 disease-drug rules and 2141 symptom-drug rules were obtained, indicating that both diseases and symptoms are strongly associated with drugs. Intersection analysis showed that 33.7% of the disease-drug rules were symptom-independent, while 34.6% of the symptom-drug rules were disease-independent. The basic clinical treatment pattern consists of three parts: (1) The combination of disease and symptomatic medication pattern. (2) Independent disease medication pattern. (3) Independent symptomatic medication pattern.