Realization of a service for the long-term risk assessment of diabetes-related complications
Abstract
Aim We present a computerized system for the assessment of the long-term risk of developing diabetes-related complications. Methods The core of the system consists of a set of predictive models, developed through a data-mining/machine-learning approach, which are able to evaluate individual patient profiles and provide personalized risk assessments. Missing data is a common issue in (electronic) patient records, thus the models are paired with a module for the intelligent management of missing information. Results The system has been deployed and made publicly available as Web service, and it has been fully integrated within the diabetes-management platform developed by the European project REACTION. Preliminary usability tests showed that the clinicians judged the models useful for risk assessment and for communicating the risk to the patient. Furthermore, the system performs as well as the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine when both systems are tested on an independent cohort of UK diabetes patients. Conclusions Our work provides a working example of risk-stratification tool that is (a) specific for diabetes patients, (b) able to handle several different diabetes related complications, (c) performing as well as the widely known UKPDS Risk Engine on an external validation cohort.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several studies utilized risk factors (mainly from EHRs) to develop AI algorithms to predict various types of diabetes-related complications with favorable predictive performance.
“In the prediction of diabetic complications, several studies tried to utilize risk factors (mainly from EHRs) to develop AI algorithms to predict various types of diabetes-related complications with favorable predictive performance.94,95,96,97,98,99,100”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [94]
Lagani et al. reported that an ANN using risk factors to predict diabetes-related complications achieved internal validation C index between 0.66 and 0.833 (all significantly different from 0.5, p ≤ 0.0001), and external validation in 49 T1D patients showed similarly high and statistically significant C indexes (p < 0.05 except hypoglycemia and CVD models with p = 0.0584 and 0.0932), with good T2D
“the results of the internal validation (T1D) reported a C index between 0.66 and 0.833, all statistically significantly different from 0.5 (p ≤ 0.0001); these results were further corroborated by the external validation (49 patients with T1D), where all models obtained similarly high and statistically significant C indexes (p < 0.05 except for the hypoglycemia and CVD model, with p = 0.0584 and 0.”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Lagani et al.94
Cites
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008) · The Annals of Applied Statistics · doi:10.1214/08-aoas169 · Cited by 2,714 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Machine learning and data mining methods in diabetes research
Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445 · see citing works
Artificial intelligence for diabetes management and decision support: Literature review
Contreras, I., & Vehi, J. (2018) · Journal of Medical Internet Research · doi:10.2196/10775 · Cited by 560 · see citing works
Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review
Yin, J., Ngiam, K. Y., & Teo, H. H. (2021) · Journal of Medical Internet Research · doi:10.2196/25759 · Cited by 419 · see citing works
Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · see citing works
- Authors
- Lagani, V., Chiarugi, F., Manousos, D., Verma, V., Fursse, J., Marias, K., & Tsamardinos, I.
- Affiliations
- Foundation for Research and Technology Hellas (GR)
- Brunel University of London (GB)
- University of Crete (GR)
- Corresponding author
- Lagani, Vincenzo
- Venue
- Journal of Diabetes and its Complications
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 29
- Issue
- 5
- Pages
- 691-698
- ISSN
- 0891-6632 (ISSN-L)
- 1056-8727
- OpenAlex
- W1915356268
- Cited by
- 34
- References
- 1
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 4 · 2023: 2 · 2022: 1 · 2021: 6 · 2020: 11 · 2019: 1 · 2018: 4 · 2017: 1
- FWCI
- 3.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Diabetes and its Complications (publishedVersion)
- PubMed (publishedVersion)
- Funders
- European Commission
- General Secretariat for Research and Technology
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases
- NIH Clinical Center
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- diabetes complications, long-term risk assessment, prediction model, individual risk assessment, missing data handling, electronic health records, web services, risk stratification, UKPDS risk engine
- MeSH terms
- Machine Learning, Bayes Theorem, Combined Modality Therapy, Decision Making, Computer-Assisted, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Female, Humans, Male, Models, Biological, Risk Factors, Risk Assessment, Internet, Diabetes Complications, Data Mining, Precision Medicine, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
