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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 2 of 2 Research Studies DisplayedNarindrarangkura P, Alafaireet PE, Khan U
Predicting suicide attempts among people with diabetes using a large multicenter electronic health records dataset.
This study’s goal was to determine the risk factors for suicidal behaviors of people with diabetes as they have a higher risk than the general population. The authors investigated risk factors and predicted suicide attempts in people with diabetes using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. They used data from Cerner Real-World Data™ and included over 3 million diabetes patients in the study. They analyzed gender-, diabetes-type, and depression-specific LASSO regression models. The study included 7764 subjects diagnosed with suicide attempts with an average age of 45. They found risk factors for suicide attempts in diabetes patients, such as being an American Indian or Alaska Native, atypical agents, benzodiazepines, and antihistamines. Amyotrophy had a negative coefficient for suicide attempts with males with diabetes but had a positive coefficient for females. Using MAOI had a negative coefficient for suicide attempts in T1DM patients. Patients less than 20 years of age had a positive coefficient for suicide in depressed and non-depressed patients with diabetes.
AHRQ-funded; HS028032.
Citation: Narindrarangkura P, Alafaireet PE, Khan U .
Predicting suicide attempts among people with diabetes using a large multicenter electronic health records dataset.
Int J Psychiatry Med 2023 Jul; 58(4):302-24. doi: 10.1177/00912174231162477..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Behavioral Health, Diabetes, Chronic Conditions
Yoon S, Taha B, Bakken S
Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression.
The purposes of this methodological paper are: 1) to describe data mining methods for building a classification model for a chronic disease using a U.S. behavior risk factor data set, and 2) to illustrate application of the methods using a case study of depressive disorder. Its application of data mining strategies identified childhood experience living with mentally ill and sexual abuse, and limited usual activity as the strongest correlates of depression among hundreds of variables.
AHRQ-funded; HS019853; HS022961.
Citation: Yoon S, Taha B, Bakken S .
Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression.
Stud Health Technol Inform 2014;201:71-8..
Keywords: Chronic Conditions, Behavioral Health, Depression, Health Information Technology (HIT), Electronic Health Records (EHRs)