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Medication Use and the Risk of Newly Diagnosed Diabetes in Patients with Epilepsy: A Data Mining Application on a Healthcare Database

Medication Use and the Risk of Newly Diagnosed Diabetes in Patients with Epilepsy: A Data Mining Application on a Healthcare Database
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Author(s): Sheng-Feng Sung (Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi, Taiwan & National Chung Cheng University, Minxiong, Taiwan), Pei-Ju Lee (National Chung Cheng University, Minxiong, Taiwan), Cheng-Yang Hsieh (Tainan Sin Lau Hospital and National Cheng Kung University Hospital and College of Medicine, Tainan, Taiwan) and Wan-Lun Zheng (National Chung Cheng University, Minxiong, Taiwan)
Copyright: 2020
Volume: 32
Issue: 2
Pages: 16
Source title: Journal of Organizational and End User Computing (JOEUC)
Editor(s)-in-Chief: Sang-Bing Tsai (University of Electronic Science and Technology of China Zhongshan Institute, China and Research Center for Environment and Sustainable Development, Civil Aviation University of China, China)
DOI: 10.4018/JOEUC.2020040105

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Abstract

Epilepsy is a common neurological disorder that affects millions of people worldwide. Patients with epilepsy generally require long-term antiepileptic therapy and many of them receive polypharmacy. Certain medications, including older-generation antiepileptic drugs, have been known to predispose patients to developing diabetes. Although data mining techniques have become widely used in healthcare, they have seldom been applied in this clinical problem. Here, the authors used association rule mining to discover drugs or drug combinations that may be associated with newly diagnosed diabetes. Their findings indicate in addition to the most common culprits such as phenytoin and valproic acid, prescriptions containing carbamazepine, oxcarbazepine, or lamotrigine may be related to the development of newly diagnosed diabetes. These mined rules are useful as guidance to both clinical practice and future research.

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