In 2019, 37.3 million Americans had diabetes mellitus, and 1.2 million Americans aged 18 and older had attempted suicide in the past year. According to previous studies, people with type 1 diabetes were three to four times more likely to attempt suicide, and newly diagnosed with type 2 diabetes were two times more likely to attempt suicide when compared with the general population. However, understanding the relationship between suicide attempts and other risk factors for people with diabetes is still lacking.
In medical research, the data mining technique has become a promising way to effectively analyze high-dimensional data by extracting meaningful patterns to help with clinical decision-making. Subgroup discovery is one of the data mining techniques that aims to find interesting and interpretable patterns represented in the form of rules for the outcome of interest.
This proposed study aims to identify patterns associated with the subgroup population of those patients with suicide attempts among people with diabetes using multiple subgroup discovery techniques. This study will fill the knowledge gap about the relationship between suicide attempts and diabetes. This study will provide practical patterns of the subgroup that are understandable for providers to provide suicide prevention during outpatient clinic visits.
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