AIMC Topic: Diabetes Mellitus, Type 2
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Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: A retrospective cohort study.
PloS one
Feb 21, 2023
Patients with type 2 diabetes mellitus (T2DM) have more than twice the risk of developing heart failure (HF) compared to patients without diabetes. The present study is aimed to build an artificial intelligence (AI) prognostic model that takes in acc...
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes.
Artificial intelligence in medicine
Feb 2, 2023
Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by 'contextual explanations' that let the practitioner connect system inferences to their context of use. However, their importance in improving mo...
Implementation of five machine learning methods to predict the 52-week blood glucose level in patients with type 2 diabetes.
Frontiers in endocrinology
Jan 20, 2023
OBJECTIVE: For the patients who are suffering from type 2 diabetes, blood glucose level could be affected by multiple factors. An accurate estimation of the trajectory of blood glucose is crucial in clinical decision making. Frequent glucose measurem...
Nailfold capillaroscopy and deep learning in diabetes.
Journal of diabetes
Jan 15, 2023
OBJECTIVE: To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and its complications.
Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.
Nature biotechnology
Jan 2, 2023
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes int...
Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance.
Journal of diabetes science and technology
Dec 23, 2022
BACKGROUND: Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not ...
Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes.
Nature communications
Nov 14, 2022
Type-2 diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4 of the total healthcare spending in the United States (US). Current treatment guidelines endorse a massive number of potential anti-...
Predictive Analysis of Diabetes-Risk with Class Imbalance.
Computational intelligence and neuroscience
Oct 11, 2022
Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main characteristic caused by insufficient insulin secretion and poses a serious risk to human health. The...
Longitudinal deep learning clustering of Type 2 Diabetes Mellitus trajectories using routinely collected health records.
Journal of biomedical informatics
Oct 8, 2022
Type 2 diabetes mellitus (T2DM) is a highly heterogeneous chronic disease with different pathophysiological and genetic characteristics affecting its progression, associated complications and response to therapies. The advances in deep learning (DL) ...
Predicting poor glycemic control during Ramadan among non-fasting patients with diabetes using artificial intelligence based machine learning models.
Diabetes research and clinical practice
Jul 6, 2022
AIMS: This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.
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