Association rule mining of real-world data: Uncovering links between race, glycemic control, lipid profiles, and suicide attempts in individuals with diabetes

Ploypun Narindrarangkura; Patricia E Alafaireet; Uzma Khan; Min Soon Kim

doi:10.1016/j.imu.2023.101345

Association rule mining of real-world data: Uncovering links between race, glycemic control, lipid profiles, and suicide attempts in individuals with diabetes

Inform Med Unlocked. 2023:42:101345. doi: 10.1016/j.imu.2023.101345. Epub 2023 Sep 1.

Authors

Ploypun Narindrarangkura¹, Patricia E Alafaireet², Uzma Khan^{3

4}, Min Soon Kim^{2

5}

Affiliations

¹ Phramongkutklao College of Medicine, 317 Ratchawithi Rd, Thung Phaya Thai, Ratchathewi, Bangkok, 10400, Thailand.
² Department of Health Management and Informatics, University of Missouri, 5 Hospital Drive, Columbia, MO, 65212, USA.
³ Cosmopolitan International Diabetes and Endocrinology Center, USA.
⁴ Department of Medicine, University of Missouri, 5 Hospital Drive, Columbia, MO, 65212, USA.
⁵ University of Missouri Institute for Data Science and Informatics, 5 Hospital Drive, Columbia, MO, 65212, USA.

Abstract

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.

Keywords: Association rule; Data mining; Diabetes; Risk pattern; Suicide attempts.

Abstract

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