AIMC Topic: Diabetes Complications

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Combining glass box and black box evaluations in the identification of heart disease risk factors and their temporal relations from clinical records.

Journal of biomedical informatics Jul 2, 2015
BACKGROUND: The determination of risk factors and their temporal relations in natural language patient records is a complex task which has been addressed in the i2b2/UTHealth 2014 shared task. In this context, in most systems it was broadly decompose...
Narration Natural Language Processing Confidentiality Data Mining Pattern Recognition, Automated France Computer Security Vocabulary, Controlled Diabetes Complications Risk Assessment Comorbidity Incidence Cohort Studies Longitudinal Studies Female Aged Male Cardiovascular Diseases Middle Aged Humans Algorithms Electronic Health Records
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Using local lexicalized rules to identify heart disease risk factors in clinical notes.

Journal of biomedical informatics Jun 29, 2015
Heart disease is the leading cause of death globally and a significant part of the human population lives with it. A number of risk factors have been recognized as contributing to the disease, including obesity, coronary artery disease (CAD), hyperte...
Semantics Pattern Recognition, Automated Computer Security Vocabulary, Controlled Diabetes Complications Female Narration Aged Natural Language Processing Male Confidentiality Cardiovascular Diseases Risk Assessment Data Mining Middle Aged Comorbidity United Kingdom Incidence Humans Cohort Studies Electronic Health Records Longitudinal Studies
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The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

Journal of biomedical informatics Jun 26, 2015
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...
Supervised Machine Learning Data Mining Pattern Recognition, Automated Humans Computer Security Electronic Health Records Vocabulary, Controlled Female Diabetes Complications Aged Longitudinal Studies Maryland Male Narration Middle Aged Risk Assessment Comorbidity Natural Language Processing Incidence Coronary Artery Disease Cohort Studies Confidentiality
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Annotating risk factors for heart disease in clinical narratives for diabetic patients.

Journal of biomedical informatics May 21, 2015
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm...
Data Mining Documentation New York Pattern Recognition, Automated Computer Security Longitudinal Studies Narration Humans Natural Language Processing Middle Aged Electronic Health Records Coronary Artery Disease Vocabulary, Controlled Risk Assessment Female Confidentiality Comorbidity Diabetes Complications Aged Incidence Boston Cohort Studies Male
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Applying a novel combination of techniques to develop a predictive model for diabetes complications.

PloS one Apr 22, 2015
Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according ...
Models, Theoretical Humans Regression Analysis Neural Networks, Computer Predictive Value of Tests Algorithms Bayes Theorem Risk Factors Diabetes Complications Risk Assessment
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Development and validation of a convenient dementia risk prediction tool for diabetic population: A large and longitudinal machine learning cohort study.

Journal of affective disorders Jul 1, 2025
BACKGROUND: Diabetes mellitus has been shown to increase the risk of dementia, with diabetic patients demonstrating twice the dementia incidence rate of non-diabetic populations. We aimed to develop and validate a novel machine learning-based dementi...
Risk Assessment Female Diabetes Complications Humans Risk Factors Dementia Machine Learning Aged Diabetes Mellitus Prospective Studies Male Longitudinal Studies Middle Aged United Kingdom
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A machine learning-based risk prediction model for diabetic oral ulceration.

BMC oral health May 22, 2025
BACKGROUND: Diabetic oral ulceration (DOU) is a prevalent and debilitating complication among diabetic patients, significantly impairing their quality of life and imposing substantial economic burdens. Studies indicate that over 90% of diabetic patie...
Humans Male Middle Aged Risk Assessment Support Vector Machine Oral Ulcer Diabetes Complications Machine Learning Female Risk Factors Aged Adult
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Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

World journal of gastroenterology Mar 14, 2025
BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). Mitochondrial dysfunction impairs oxidative phosphorylation and increases ...
Non-alcoholic Fatty Liver Disease DNA, Mitochondrial Diabetes Complications Boosting Machine Learning Algorithms Case-Control Studies Cohort Studies Polymorphism, Single Nucleotide Logistic Models Random Forest Humans Adult Male Middle Aged
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Machine Learning Identifies Metabolic Dysfunction-Associated Steatotic Liver Disease in Patients With Diabetes Mellitus.

The Journal of clinical endocrinology and metabolism Jul 12, 2024
CONTEXT: The presence of metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with diabetes mellitus (DM) is associated with a high risk of cardiovascular disease, but is often underdiagnosed.
Humans Machine Learning Middle Aged Risk Assessment ROC Curve Diabetes Mellitus, Type 2 Fatty Liver Female Risk Factors Aged Adult Male Diabetes Mellitus Diabetes Complications
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Predicting complications of diabetes mellitus using advanced machine learning algorithms.

Journal of the American Medical Informatics Association : JAMIA Jul 1, 2020
OBJECTIVE: We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development.
Diabetes Mellitus, Type 2 Decision Trees Diabetes Complications Neural Networks, Computer Algorithms Prognosis Risk Assessment Deep Learning Humans
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