Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

Stolfi, Paola; Valentini, Ilaria; Palumbo, Maria Concetta; Tieri, Paolo; Grignolio, Andrea; Castiglione, Filippo

doi:10.1186/s12859-020-03763-4

Cited by 16 publications

(7 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a result, it can provide valuable insights into an individual's immune status and potential outcomes in response to specific stimuli. The C-IMMSIM model, in this context, offers a means to simulate a cohort of virtual individuals (referred to as a virtual cohort ) for the study of specific pathologic and therapeutic conditions, such as type 2 diabetes ( 24 , 25 ) or COVID-19 vaccination ( 26 ).…”

Section: Examples Of Ongoing Mdt Projectsmentioning

confidence: 99%

Toward mechanistic medical digital twins: some use cases in immunology

Laubenbacher,

Adler,

et al. 2024

Front. Digit. Health

Self Cite

View full text Add to dashboard Cite

A fundamental challenge for personalized medicine is to capture enough of the complexity of an individual patient to determine an optimal way to keep them healthy or restore their health. This will require personalized computational models of sufficient resolution and with enough mechanistic information to provide actionable information to the clinician. Such personalized models are increasingly referred to as medical digital twins. Digital twin technology for health applications is still in its infancy, and extensive research and development is required. This article focuses on several projects in different stages of development that can lead to specific—and practical–medical digital twins or digital twin modeling platforms. It emerged from a two-day forum on problems related to medical digital twins, particularly those involving an immune system component. Open access video recordings of the forum discussions are available.

show abstract

Section: Examples Of Ongoing Mdt Projectsmentioning

confidence: 99%

Toward mechanistic medical digital twins: some use cases in immunology

Laubenbacher,

Adler,

et al. 2024

Front. Digit. Health

Self Cite

View full text Add to dashboard Cite

show abstract

“…Stolfi et al [32] This article examines the various variables contributing to the onset and progression of diabetes. To do this, the scientists created a computer model that resembles the immunological and metabolic alterations linked to the disease and outlines its origin.…”

Section: Anthimopoulos Et Al [29]mentioning

confidence: 99%

Glucose Monitoring with AI Analytics for Diabetes Management Using Machine Learning and Smart Devices

Contreras,

Vehi

2023

IJANER

View full text Add to dashboard Cite

Over the past few years, there has been a noticeable growth in the usage of smart devices for diabetes treatment. These technologies are meant to make life easier for people with diabetes because they have the potential to improve the stability of blood sugar monitoring and predict the onset of dangerous episodes (hypo/hyperglycemia). Notwithstanding, the primary goals of diabetic self-management are to enhance the lifestyle and quality of life of those living with the disease. This study used the literature that addressed diabetes to conduct a systematic review to monitor and control the disease effectively. The search was narrowed to include topical keywords like Artificial Intelligence (AI), technology, self-management, and diabetes, for which PubMed databases were used. There were 2655 papers in all, released between 2013 and 2023. Predicting blood glucose, identifying risk events early, automatically adjusting insulin dosages, and other diabetes care issues are the main goals of most of the chosen research. Wearable technology and AI methods were combined in these investigations. Much scientific attention has been drawn to wearable technology like Continuous Glucose Monitoring (CGM) in treating chronic illnesses like diabetes. Not only may they help avoid diabetes-related problems, but they can also aid in managing diabetes. Utilizing these gadgets has also enhanced the quality of life and treatment of sickness.

show abstract

“…In a distinct work by Bernardini et al ( 2020a , b ), Triglyceride Glucose Index (TyG) a clinically significant but less exploited diabetes indicator is used to predict diabetes risk from historical EHR data using multiple-instance learning boosting algorithm. Stolfi et al ( 2020 ) developed a computational model to simulate T2D patients to study the immunological and metabolic altercations expressed through clinical, physiological and behavioral features. They attempted to extend the EU-funded “Multi-scale Immune System Simulator for the Onset of Type 2 diabetes” (MISSION-T2D) model to build an economically-viable smartphone application for diabetes self- management.…”

Section: Machine Learning For Diabetes Diagnosismentioning

confidence: 99%

Machine learning for diabetes clinical decision support: a review

Tuppad

Patil

2022

Adv. in Comp. Int.

View full text Add to dashboard Cite

Type 2 diabetes has recently acquired the status of an epidemic silent killer, though it is non-communicable. There are two main reasons behind this perception of the disease. First, a gradual but exponential growth in the disease prevalence has been witnessed irrespective of age groups, geography or gender. Second, the disease dynamics are very complex in terms of multifactorial risks involved, initial asymptomatic period, different short-term and long-term complications posing serious health threat and related co-morbidities. Majority of its risk factors are lifestyle habits like physical inactivity, lack of exercise, high body mass index (BMI), poor diet, smoking except some inevitable ones like family history of diabetes, ethnic predisposition, ageing etc. Nowadays, machine learning (ML) is increasingly being applied for alleviation of diabetes health burden and many research works have been proposed in the literature to offer clinical decision support in different application areas as well. In this paper, we present a review of such efforts for the prevention and management of type 2 diabetes. Firstly, we present the medical gaps in diabetes knowledge base, guidelines and medical practice identified from relevant articles and highlight those that can be addressed by ML. Further, we review the ML research works in three different application areas namely—(1) risk assessment (statistical risk scores and ML-based risk models), (2) diagnosis (using non-invasive and invasive features), (3) prognosis (from normoglycemia/prior morbidity to incident diabetes and prognosis of incident diabetes to related complications). We discuss and summarize the shortcomings or gaps in the existing ML methodologies for diabetes to be addressed in future. This review provides the breadth of ML predictive modeling applications for diabetes while highlighting the medical and technological gaps as well as various aspects involved in ML-based diabetes clinical decision support.

show abstract

Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices

Cited by 16 publications

References 63 publications

Toward mechanistic medical digital twins: some use cases in immunology

Toward mechanistic medical digital twins: some use cases in immunology

Glucose Monitoring with AI Analytics for Diabetes Management Using Machine Learning and Smart Devices

Machine learning for diabetes clinical decision support: a review

Contact Info

Product

Resources

About