2022
DOI: 10.7150/ijms.71341
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A Clinical Decision Support System for Diabetes Patients with Deep Learning: Experience of a Taiwan Medical Center

Abstract: Background: Diabetes mellitus (DM) is a major public health problem worldwide. It involves dysfunction of blood sugar regulation resulting from insulin resistance, inadequate insulin secretion, or excessive glucagon secretion. Methods: This study collated 971,401 drug usage records of 51,009 DM patients. These data include patient identification code, age, gender, outpatient visiting dates, visiting code, medication features (included items, doses, and frequencies of drugs), HbA1c results, and testing time. We… Show more

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Cited by 6 publications
(2 citation statements)
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“…Machine learning models also only sometimes provide predictions at the point of care, and while it can be updated regularly, it is not generally auto-adapting as new EHRs are added. One example of the value of machine learning analysis of EMR data is a 2022 study [11]. These authors focused their research of what drug produces the best outcome in the management of diabetes mellitus.…”
Section: Step 3 the Limitations Of Machine Learningmentioning
confidence: 99%
“…Machine learning models also only sometimes provide predictions at the point of care, and while it can be updated regularly, it is not generally auto-adapting as new EHRs are added. One example of the value of machine learning analysis of EMR data is a 2022 study [11]. These authors focused their research of what drug produces the best outcome in the management of diabetes mellitus.…”
Section: Step 3 the Limitations Of Machine Learningmentioning
confidence: 99%
“…Evidence suggests that clinical decision support systems (CDSSs) can assist clinicians in effectively monitoring patient data and making accurate and informed treatment decisions [ 7 - 9 ]. Traditionally, CDSSs have relied on medical expertise and clinical practice guidelines.…”
Section: Introductionmentioning
confidence: 99%