AIMC Topic: Diabetes Mellitus
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Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.
Scientific reports
Nov 22, 2024
This paper addresses the pressing issue of diabetes, which is a widespread condition affecting a huge population worldwide. As cells become less responsive to insulin or fail to produce it adequately, blood sugar levels rise. This has the potential t...
Enhancing the accuracy of blood-glucose tests by upgrading FTIR with multiple-reflections, quantum cascade laser, two-dimensional correlation spectroscopy and machine learning.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Nov 5, 2024
The accuracy of screening diabetes from non-diabetes is drastically enhanced by strategically upgrading the bench-marking infrared spectroscopy technique for non-invasive tests of blood-glucose, both with state-of-the-art instrumentation-retrofits an...
Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications.
Methods (San Diego, Calif.)
Nov 1, 2024
Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) ...
Pioneering diabetes screening tool: machine learning driven optical vascular signal analysis.
Biomedical physics & engineering express
Nov 1, 2024
The escalating prevalence of diabetes mellitus underscores the critical need for non-invasive screening tools capable of early disease detection. Present diagnostic techniques depend on invasive procedures, which highlights the need for advancement o...
Association Between Body Composition Measured by Artificial Intelligence and Long-Term Sequelae After Acute Pancreatitis.
Digestive diseases and sciences
Oct 22, 2024
BACKGROUND/OBJECTIVES: The clinical utility of body composition in the development of complications of acute pancreatitis (AP) remains unclear. We aimed to describe the associations between body composition and the recurrence of AP.
A machine learning tool for identifying newly diagnosed heart failure in individuals with known diabetes in primary care.
ESC heart failure
Oct 20, 2024
AIMS: We aimed to create a predictive model utilizing machine learning (ML) to identify new cases of congestive heart failure (CHF) in individuals with diabetes in primary health care (PHC) through the analysis of diagnostic data.
Impact of a clinical pharmacist-led, artificial intelligence-supported medication adherence program on medication adherence performance, chronic disease control measures, and cost savings.
Journal of the American Pharmacists Association : JAPhA
Oct 18, 2024
BACKGROUND: Chronic diseases are the leading cause of disability and death in the United States. Clinical pharmacists have been shown to optimize health outcomes and reduce health care expenditures in patients with chronic diseases through improving ...
Estimating the prevalence of select non-communicable diseases in Saudi Arabia using a population-based sample: econometric analysis with natural language processing.
Annals of Saudi medicine
Oct 3, 2024
BACKGROUND: Non-communicable diseases (NCDs) are a major public health challenge globally, including in Saudi Arabia. However, measuring the true extent of NCD prevalence has been hampered by a paucity of nationally representative epidemiological stu...
Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm.
PloS one
Sep 30, 2024
Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuabl...
Predicting the risk of diabetes complications using machine learning and social administrative data in a country with ethnic inequities in health: Aotearoa New Zealand.
BMC medical informatics and decision making
Sep 27, 2024
BACKGROUND: In the age of big data, linked social and administrative health data in combination with machine learning (ML) is being increasingly used to improve prediction in chronic disease, e.g., cardiovascular diseases (CVD). In this study we aime...
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