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:10.1109/bibm47256.2019.8983035

Cited by 6 publications

(6 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI technologies have gained popularity in facilitating prediction in Web-based applications. For example, based on the analysis of time course of 46,170 virtual subjects who experienced varied lifestyle conditions using decision trees, random forests, Stolfi et al [ 63 ] highlighted machine learning models’ effectiveness for predicting the synthetic dataset as a computationally cheaper “mathematical model to be implemented on mobile devices to allow self-assessment by informed and aware individuals (p. 508)”. A step-by-step analysis [ 64 ] indicated the feasibility of user journey data analysis in varied machine learning models to predict dropout in digital health interventions.…”

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

View full text Add to dashboard Cite

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

show abstract

Section: Resultsmentioning

confidence: 99%

Machine and cognitive intelligence for human health: systematic review

Chen

Cheng

Wang³

et al. 2022

Brain Inf.

View full text Add to dashboard Cite

show abstract

“…( 1 ) plus the time. One of the most successful models for these kinds of dataset, namely datasets which have a moderate number of features and a huge number of items, is the random forest [ 33 ]), which has already shown great fitting performance in [ 10 ]. Therefore we construct our emulator using a random forest algorithm in the last of the four methodologies briefly described above [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

“…The proposed adaptive sampling is based on the idea of iteratively selecting data to add to a starting training set having more prediction uncertainty. In [ 10 ] we used a random forest to predict and analyse the impact of the input variables on the dynamics of a complex multi-scale simulation model, being a mixture of ordinary differential equations and agent-based modelling, able to predict the risk of type-2 diabetes (T2D). The mentioned computational model (herein referred to as M-T2D) has been implemented to take into account a set of user input data and to subsequently provide an estimation of the risk to develop a T2D clinical picture.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Emulating complex simulations by machine learning methods

Stolfi

Castiglione

2021

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic and inflammatory processes underlying the development of type-2 diabetes in absence of familiarity. Given the very high incidence of type-2 diabetes, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of the disease for aware individuals. The high computational cost of the developed model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, makes the simulator executable only on powerful workstations but not on mobile devices. Hence the need to implement an emulator with a reduced computational cost that can be executed on mobile devices to provide real-time self-monitoring. Results Similarly to our previous work, we propose an emulator based on a machine learning algorithm but here we consider a different approach which turn out to have better performances, indeed in terms of root mean square error we have an improvement of two order magnitude. We tested the proposed emulator on samples containing different number of simulated trajectories, and it turned out that the fitted trajectories are able to predict with high accuracy the entire dynamics of the simulator output variables. We apply the emulator to control the level of inflammation while leveraging on the nutritional input. Conclusion The proposed emulator can be implemented and executed on mobile health devices to perform quick-and-easy self-monitoring assessments.

show abstract

“…Stolfi et al [ 3 ] investigate the mechanisms involved in the onset of type 2 diabetes in the absence of familiarity. This has led to the development of a computational model that recapitulates the aetiology of the disease and simulates the immunological and metabolic alterations linked to type-2 diabetes subject to clinical, physiological, and behavioural features of prototypical human individuals.…”

Section: Topics Coveredmentioning

confidence: 99%

Toward computational modelling on immune system function

2020

View full text Add to dashboard Cite

The 3rd edition of the computational methods for the immune system function workshop has been held in San Diego, CA, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) from November 18 to 21, 2019. The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. Among these, the best 10 papers have been selected and extended for presentation in this special issue. The covered topics range from computer-aided identification of T cell epitopes to the prediction of heart rate variability to prevent brain injuries, from In Silico modeling of Tuberculosis and generation of digital patients to machine learning applied to predict type-2 diabetes risk.

show abstract

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

Cited by 6 publications

References 58 publications

Machine and cognitive intelligence for human health: systematic review

Machine and cognitive intelligence for human health: systematic review

Emulating complex simulations by machine learning methods

Toward computational modelling on immune system function

Contact Info

Product

Resources

About