相关论文: Behavioral-clinical phenotyping with type 2 diabet…
Sustained high levels of blood glucose in type 2 diabetes (T2DM) can have disastrous long-term health consequences. An essential component of clinical interventions for T2DM is monitoring dietary intake to keep plasma glucose levels within…
The classification of diabetes and prediabetes by static glucose thresholds obscures the pathophysiological dysglycemia heterogeneity, primarily driven by insulin resistance (IR), beta-cell dysfunction, and incretin deficiency. This review…
Diabetes mellitus affects over 537 million adults worldwide and remains a major challenge in preventive healthcare. Existing machine-learning studies primarily formulate diabetes prediction as a binary classification problem, while…
Precise and timely forecasting of blood glucose levels is essential for effective diabetes management. While extensive research has been conducted on Type 1 diabetes mellitus, Type 2 diabetes mellitus (T2DM) presents unique challenges due…
Type 1 diabetes (T1D) management can be significantly enhanced through the use of predictive machine learning (ML) algorithms, which can mitigate the risk of adverse events like hypoglycemia. Hypoglycemia, characterized by blood glucose…
Type 1 Diabetes (T1D) is an autoimmune disease leading to insulin insufficiency. Thus, patients require lifelong insulin therapy, which has a side effect of hypoglycemia. Hypoglycemia is a critical state of decreased blood glucose levels…
Purpose: Although elevated BMI is a well-known risk factor for type 2 diabetes, the disease's presence in some lean adults and absence in others with obesity suggests that detailed body composition may uncover abdominal phenotypes of type 2…
Type 1 diabetes (T1D) is an autoimmune disorder that leads to the destruction of insulin-producing cells, resulting in insulin deficiency, as to why the affected individuals depend on external insulin injections. However, insulin can…
A task of vital clinical importance, within Diabetes management, is the prevention of hypo/hyperglycemic events. Increasingly adopted Continuous Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time insights into a…
Obesity is a major health problem, increasing the risk of various major chronic diseases, such as diabetes, cancer, and stroke. While the role of obesity identified by cross-sectional BMI recordings has been heavily studied, the role of BMI…
Diabetes is one of the most prevalent diseases worldwide, characterized by persistently high blood sugar levels, capable of damaging various internal organs and systems. Diabetes patients require routine check-ups, resulting in a time…
The LIPGENE-SU.VI.MAX study, like many others, recorded high dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account for both phenotypic and genetic factors when studying the…
With the increasing availability of wearable devices, continuous monitoring of individuals' physiological and behavioral patterns has become significantly more accessible. Access to these continuous patterns about individuals' statuses…
Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are…
Effective management of Type 1 Diabetes requires continuous glucose monitoring and precise insulin adjustments to prevent hyperglycemia and hypoglycemia. With the growing adoption of wearable glucose monitors and mobile health applications,…
We aim to develop clustering models to obtain behavioral representations from continuous multimodal mobile sensing data towards relapse prediction tasks. The identified clusters could represent different routine behavioral trends related to…
Trace clustering has increasingly been applied to find homogenous process executions. However, current techniques have difficulties in finding a meaningful and insightful clustering of patients on the basis of healthcare data. The resulting…
Background: Accurate week-ahead forecasts of continuous glucose monitoring (CGM) derived metrics could enable proactive diabetes management, but relative performance of modern tabular learning approaches is incompletely defined. Methods: We…
The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety, depression, loneliness, and sleep quality, as these platforms increasingly influence…
Early detection of chronic diseases is beneficial to healthcare by providing a golden opportunity for timely interventions. Although numerous prior studies have successfully used machine learning (ML) models for disease diagnoses, they…