Year: 2023 Source: Informatics in Medicine Unlocked. (2023). 42, 101345. SIEC No: 20231980
Aims: The increased risk of suicide among individuals with diabetes is a significant public health concern. However, few studies have focused on understanding the relationship between suicide attempts and diabetes. Association rule mining (ARM) is a data  mining technique to discover a set of high-risk factors of a given disease. Therefore, this study aimed to utilize ARM to identify a high-risk group of suicide attempts among patients with diabetes using Cerner Real-World Data™ (CRWD). Methods: The study  analyzed a large multicenter electronic health records data of 3,265,041 patients with diabetes from 2010 to 2020. The Least Absolute Shrinkage and Selection Operator regression with ten-fold cross validation and the Apriori algorithm with ARM were used to uncover groups of high-risk suicide attempts. Results: Of the 52,217,517 unique patients in the CRWD, 3,266,856 were diagnosed with diabetes. There were 7764 (0.2%) patients with diabetes who had a history of suicide attempts. The study revealed that patients with diabetes who were never married  and had average blood glucose levels below 150 mg/dl were more likely to attempt suicide. In contrast, patients with diabetes aged 60 and older who had diabetes for less than five years and A1C levels between 6.5 and 8.9% were less likely to attempt suicide.  Risk factors were strongly associated with suicide attempts, including never married, White, blood glucose levels below 150 mg/dl, and LDL levels below 100 mg/dl. Conclusions: This is the first study utilizing ARM to discover the risk patterns for suicide attempts in individuals with diabetes. ARM showed the potential for knowledge discovery in large multi-center electronic health records data. The results are  explainable and could be practically used by providers during outpatient clinic visits. Further studies are needed to validate the results and investigate the cause-and-effect relationship of suicide attempts among individuals with diabetes