Prediction of neuropathy, neuropathic pain and kinesiophobia in patients with type 2 diabetes and design of computerized clinical decision support systems by using artificial intelligence
Abstract
Exercise is a key component for prevention and treatment of type 2 diabetes. However, diabetes complications affect exercise habits. Computerized clinical decision support systems (CCDSSs) may help specialists improve their decision-making abilities in the management of diseases. We hypothesized that patients' diabetic neuropathy, neuropathic pain, and kinesiophobia will quickly be identified in the early stages by using the designed CCDSSs. It is thought that such systems will help in planning exercise programs for patients with diabetes and in maintaining the appropriate programs. Based on our hypothesis, we conclude that CCDSSs will also be effective in managing complications and movement dysfunctions occurring in the musculoskeletal system.
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Ozdemir et al. (2020) reported the same result for AUC and accuracy, resulting in an almost perfect classification performance of 0.99.
“Ozdemir et al. (2020) reported the same result for AUC and accuracy resulting in an almost perfect classification performance of 0.99.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Ozdemir et al. (2020)
Excluding Ozdemir et al. (2020), the prediction performance in terms of AUC for the best model in the included studies ranges from 0.685 to 0.847.
“If we exclude extremely optimistic results from Ozdemir et al. (2020), we can conclude with the observation that prediction performance in terms of AUC for the best model in the study ranges from 0.685 up to 0.847.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Ozdemir et al. (2020)
The robustness of Ozdemir et al.'s evaluation protocol is difficult to argue because no data or code was available.
“Since no data or code was available, it is difficult to argue the robustness of their evaluation protocol.”
From Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature (2022) · cites this paper as Ozdemir et al. (2020)
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- Authors
- Ozdemir, F., Ari, A., Kilcik, M. H., Hanbay, D., & Sahin, I.
- Affiliations
- Inonu University (TR)
- ORCID
- Hanbay, Davut
- Venue
- Medical Hypotheses
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 143
- Pages
- 110070
- ISSN
- 0306-9877 (ISSN-L)
- OpenAlex
- W3039566119
- Cited by
- 10
- References
- 24
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 2 · 2023: 1 · 2022: 2
- FWCI
- 0.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Hypotheses (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Pharmacology › Musculoskeletal pain and rehabilitation
- Keywords
- type 2 diabetes, diabetic neuropathy, neuropathic pain, kinesiophobia, computerized clinical decision support systems, artificial intelligence, exercise program, early prediction of complications
- MeSH terms
- Artificial Intelligence, Diabetes Mellitus, Type 2, Diabetic Neuropathies, Humans, Neuralgia, Decision Support Systems, Clinical
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