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 increased cardiovascular risk, we offer a broad overview of various data-driven methods ...
Researchers from MIT and Technion have made a significant contribution to the field of machine learning by developing an adaptive algorithm that addresses the challenge of determining when a machine should follow a teacher’s instructions or explore on its own. The algorithm autonomously decides whether...
Diabetes mellitus HIS: Presence of high signal intensity in T2-weighted magnetic resonance imaging ML: Machine learning NPV: Negative predictive value OA: Occupational Activity PASS: Patient acceptable symptom state PPV: Positive predictive value SES: Presence of snake eye sign Std: Standar...
is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify ...
Ensemble learning: a rule-based decision unit was constructed using the rules in Table 2, assigning a probability of having diabetes 1 if the conditions of the first rule apply, 0 if the conditions of the second rule apply, and 0.5 to all other cases, treated as intermediate cases. This ...
To improve the predictive performance, reduce the effect of the curse of dimensionality, and shorten learning time29, we removed the medical history of diabetes from the list of features and redeveloped the model. The predictive accuracy, positive predictive value, negative predictive value, and ...
Clustering Diagnostic Codes: Exploratory Machine Learning Approach for Preventive Care of Chronic DiseasesHigh prevalence of chronic diseases along with poor health condition and the rising diagnosis and treatment costs necessitates concentration on prevention, early detection and disease management. In this ...
The family history of diabetes was defined as diabetes history in at least a parent or sibling, the same as the family history of hypertension. Hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg, or treatment wit...
extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on CAD-related machine learning and data mining algorithms, and hopefully to ultimately advance clinical diagnosis and early treatment. To aid users, we have also built a web application ...
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 ...