Individual Prognosis of Diabetes Long-term Risks: A CBR Approach
Abstract
We present DIRAS, an application to support physicians in determining the risk of complications for individual diabetic patients. The risk pattern of each diabetic patient is obtained using a Case-based Reasoning method called LID. Case-based Reasoning is an Artificial Intelligence technique based on solving new situations according to past experiences. For each patient, the LID method determines the risk of each diabetic complication according to the risk of already diagnosed patients. In addition, LID builds a description that can be viewed as an explanation of the obtained risk.
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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 [96]
Armengol et al. reported that a case-based reasoning system using risk factors to estimate risk of complications was 100% correct in determining the kind of risk (progression or development) and the risk of stroke, 90% correct in determining amputation risk, and 72.45% correct in determining the global risk and the risk of infarct.
“the results were 100% correct in determining the kind of risk (progression or development) and the risk of stroke, 90% correct in determining amputation risk, and 72.45% correct in determining the global risk and the risk of infarct”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Armengol et al.96
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Trust building with explanation interfaces
Pu, P., & Chen, L. (2006) · Proceedings of the 11th international conference on Intelligent user interfaces · doi:10.1145/1111449.1111475 · Cited by 300 · 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
- Palaudàries, A., Plaza, E., & Armengol, E.
- Affiliations
- Consejo Superior de Investigaciones Científicas (ES)
- ORCID
- Plaza, E.
- Venue
- Methods of Information in Medicine
- Publisher
- Georg Thieme Verlag KG
- Published
- 2001
- Volume
- 40
- Issue
- 01
- Pages
- 46-51
- ISSN
- 0026-1270 (ISSN-L)
- 2511-705X
- OpenAlex
- W45084044
- Cited by
- 45
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 1 · 2019: 2 · 2017: 3 · 2015: 2 · 2014: 1 · 2012: 1
- FWCI
- 9.57
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Methods of Information in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- diabetes mellitus, diabetes complications, risk prediction, case-based reasoning, clinical decision support, long-term prognosis
- MeSH terms
- Artificial Intelligence, Diabetes Mellitus, Diagnosis, Computer-Assisted, Humans, Prognosis, Risk Assessment
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