Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at inc...
The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing ...
Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images Deep-learning models trained on retinal fundus images can be used to identify chronic kidney disease and type 2 diabetes and to predict the risk of the progressio...
The first author Mathieu Ravaut, M.Sc. of the University of Toronto and other team members stated that “The main purpose of our model was to inform population health planning and management for the prevention of diabetes that incorporates health equity. It was not our goal for this model to...
a robust machine learning framework for diabetes prediction [Paper] impact-learning: a robust machine learning algorithm [Paper] regularization helps with mitigating poisoning attacks: distributionally-robust machine learning using the wasserstein distance [Paper] robust machine learning for colorectal ...
body mass index; BAI, body adiposity index; LHR, LDL/HDL ratio; INS, insulin; IL-6, interleukin 6; NEFA, nonesterified fatty acid; hs-CRP, high-sensitivity C-reactive protein; ADP, adiponectin; MetS, metabolic syndrome; FHH, family history of hypertension; FHD, family h...
While the inclusion of the full clinical picture of patients with AKI at the scale needed to use machine learning may be challenging, phenotyping only diagnosis codes may bias the clinical picture and warrants caution in how these clusters are interpreted. A further limitation was that a low ...
This diagnostic study examines machine learning approaches applied to harmonized electronic health record data for identifying risk of atrial fibrillation.
Predicting the risk of cardiovascular disease is the key to primary prevention. Machine learning has attracted attention in analyzing increasingly large, complex healthcare data. We assessed discrimination and calibration of pre-existing cardiovascular risk prediction models and developed machine learning-base...
Machine learning techniques trained on historical patient records have demonstrated considerable potential to predict critical events in different medical domains (for example, circulatory failure, diabetes and cardiovascular disorders)11,12,13,14,15. In the mental health domain, prediction algorithms have ...