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This document provides algorithms for extracting Type 2 Diabetes Mellitus (T2DM) cases and controls from electronic medical records, including input data elements, flowcharts, and executable workflow
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How to fill out Type 2 Diabetes Mellitus Electronic Medical Record Case and Control Selection Algorithms

01
Gather all relevant patient data from the electronic medical record (EMR).
02
Identify patients meeting the diagnostic criteria for Type 2 Diabetes Mellitus.
03
Separate patients into cases (those diagnosed) and controls (those without the diagnosis).
04
Code each patient based on their clinical data, including age, BMI, and HbA1c levels.
05
Implement demographic filters to refine selection based on age, gender, and ethnicity.
06
Utilize algorithms to analyze the data for associations and candidate selection.
07
Review the selections for accuracy and completeness before finalizing.
08
Save and document the results for future reference and research purposes.

Who needs Type 2 Diabetes Mellitus Electronic Medical Record Case and Control Selection Algorithms?

01
Researchers looking to understand epidemiology and risk factors of Type 2 Diabetes Mellitus.
02
Healthcare providers aimed at improving treatment and management strategies for diabetes.
03
Public health officials analyzing diabetes trends in population studies.
04
Clinical trial coordinators selecting participants for studies related to Type 2 Diabetes Mellitus.
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People Also Ask about

It is characterized by high levels of sugar in the blood. Type 2 diabetes is also called type 2 diabetes mellitus and adult-onset diabetes.
Type 2 diabetes happens when the body cannot use insulin correctly and sugar builds up in the blood. It was once called adult-onset diabetes. Over time, high blood sugar levels in type 2 diabetes can damage the eyes, kidneys, nerves and heart.
The technical name for diabetes is diabetes mellitus. Another condition shares the term “diabetes” — diabetes insipidus — but they're distinct. They share the name “diabetes” because they both cause increased thirst and frequent urination. Diabetes insipidus is much rarer than diabetes mellitus.
These algorithms provide clinicians with a quick reference to specific diabetes care recommendations based on national guidelines and the Diabetes Standards of Care and Resources for Clinicians and Educators.
Weight loss. Losing weight can help you manage blood sugar levels, cholesterol, triglycerides and blood pressure. If you're overweight, you may see these factors improve after you lose as little as 5% of your body weight. The more weight you lose, the better it is for your health.
Finally, the LightGBM algorithm is used to train the dataset, improving the prediction accuracy for diabetic patients.

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Type 2 Diabetes Mellitus Electronic Medical Record Case and Control Selection Algorithms are systematic methods used to identify patients with type 2 diabetes within electronic medical records. These algorithms help in determining suitable cases and control participants for research studies or clinical evaluations.
Healthcare providers, clinicians, and researchers conducting studies related to type 2 diabetes are typically required to file these algorithms as part of the process to ensure accurate patient selection for research purposes.
To fill out these algorithms, the individual must gather relevant patient data from the electronic medical records, apply the selection criteria outlined in the algorithm, and record the outcomes accordingly. It is essential to ensure all data is accurate and complete.
The purpose of these algorithms is to streamline the identification of appropriate cases and controls for research, enhance the quality of data collected, and improve the overall understanding and treatment of type 2 diabetes.
The reported information typically includes patient demographics, clinical history, glucose levels, treatment regimens, and eligibility criteria based on the defined selection algorithm. This ensures comprehensive data collection for analysis.
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