Emerging Applications for Intelligent Diabetes Management

AI Magazine

It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task: (1) case-based decision support for diabetes management; (2) machine-learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The second provides an automated screen for excessive glycemic variability. The third aims to build a hypoglycemia predictor that could alert patients to dangerously low blood glucose levels in time to take preventive action.


Emerging Applications for Intelligent Diabetes Management

AI Magazine

Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task: (1) case-based decision support for diabetes management; (2) machine learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The second provides an automated screen for excessive glycemic variability. The third aims to build a hypoglycemia predictor that could alert patients to dangerously low blood glucose levels in time to take preventive action. All are products of the 4 Diabetes Support SystemTM project, which uses AI to promote the health and wellbeing of people with type 1 diabetes. These emerging applications could potentially benefit 20 million patients who are at risk for devastating complications, thereby improving quality of life and reducing health care cost expenditures.


Scientists create new sticky patch to read blood sugar levels

Daily Mail - Science & tech

Painful finger-prick blood tests taken several times a day by millions of diabetics could become a thing of the past. A pioneering new patch that sticks onto the skin to test glucose levels has been created by scientists. The device, developed by Bath University researchers, works by assessing sugar levels in sweat - not blood. It is hoped the skin patch could one day be linked to a smartphone app to warn diabetics when to take action. The patch, if proven in larger trials, could replace the current method, considered unpopular, to test sugar levels.


Long Term Management - Artificial Intelligence - RR School Of Nursing

#artificialintelligence

Type II diabetes is not an isolated disease, but rather, a complex metabolic abnormality often involving hypertension, obesity, dyslipidemia, renal function, and a spectrum of cardiovascular diseases. Appropriate management of diabetes requires multiple strategies aimed to improve the patient's glycaemic control, and minimize the risk of complications, based on individual preferences, comorbidities, and the overall prognosis. The key element for a successful outcome, however, is cooperation from the patient. Adequate information about the risks of diabetes and potential benefits of good self-management should be discussed with the patient. Basic guidelines for long-term management include diet and exercise therapies; blood glucose, blood pressure, and lipids management as described in Sect.


A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management

AAAI Conferences

Patients with diabetes must continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious short-term and long-term complications. An automatic prediction model that warned people of imminent changes in their blood glucose levels would enable them to take preventive action. In this paper, we describe a solution that uses a generic physiological model of blood glucose dynamics to generate informative features for a Support Vector Regression model that is trained on patient specific data. The new model outperforms diabetes experts at predicting blood glucose levels and could be used to anticipate almost a quarter of hypoglycemic events 30 minutes in advance. Although the corresponding precision is currently just 42%, most false alarms are in near-hypoglycemic regions and therefore patients responding to these hypoglycemia alerts would not be harmed by intervention.