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Cited by 35Artificial Intelligence in HealthcareMachine Learning in HealthcarePeripheral Neuropathies and Disorders

Identification of risk factors for patients with diabetes: diabetic polyneuropathy case study

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Metsker, Oleg, Magoev, Kirill, Yakovlev, Alexey, Yanishevskiy, Stanislav, Kopanitsa, Georgy, Kovalchuk, Sergey, Krzhizhanovskaya, Valeria V.·2020·BMC Medical Informatics and Decision Making
DOI 10.1186/s12911-020-01215-wPMC PMC7444272
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Abstract

BACKGROUND: Methods of data mining and analytics can be efficiently applied in medicine to develop models that use patient-specific data to predict the development of diabetic polyneuropathy. However, there is room for improvement in the accuracy of predictive models. Existing studies of diabetes polyneuropathy considered a limited number of predictors in one study to enable a comparison of efficiency of different machine learning methods with different predictors to find the most efficient one. The purpose of this study is the implementation of machine learning methods for identifying the risk of diabetes polyneuropathy based on structured electronic medical records collected in databases of medical information systems. METHODS: For the purposes of our study, we developed a structured procedure for predictive modelling, which includes data extraction and preprocessing, model adjustment and performance assessment, selection of the best models and interpretation of results. The dataset contained a total number of 238,590 laboratory records. Each record 27 laboratory tests, age, gender and presence of retinopathy or nephropathy). The records included information about 5846 patients with diabetes. Diagnosis served as a source of information about the target class values for classification. RESULTS: It was discovered that inclusion of two expressions, namely "nephropathy" and "retinopathy" allows to increase the performance, achieving up to 79.82% precision, 81.52% recall, 80.64% F1 score, 82.61% accuracy, and 89.88% AUC using the neural network classifier. Additionally, different models showed different results in terms of interpretation significance: random forest confirmed that the most important risk factor for polyneuropathy is the increased neutrophil level, meaning the presence of inflammation in the body. Linear models showed linear dependencies of the presence of polyneuropathy on blood glucose levels, which is confirmed by the clinical interpretation of the importance of blood glucose control. CONCLUSION: Depending on whether one needs to identify pathophysiological mechanisms for one's prospective study or identify early or late predictors, the choice of model will vary. In comparison with the previous studies, our research makes a comprehensive comparison of different decisions using a large and well-structured dataset applied to different decision support tasks.

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  • Metsker et al. reported that ANN, SVM, DT, linear regression, and logistic regression using EHR for polyneuropathy screening achieved 0.80 precision, 0.82 recall, 0.81 F1 score, 0.83 accuracy, and 0.90 AUC.

    “it could achieve 0.80 precision, 0.82 recall, 0.81 F1 score, 0.83 accuracy, and 0.90 AUC”

    From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Metsker et al.116

    The Abstract (Results) reports the five quoted metrics, rounded from 79.82% precision, 81.52% recall, 80.64% F1, 82.61% accuracy, and 89.88% AUC. However, Table 6 (Results, Influence of comorbidities) assigns those values to the decision tree; it reports different results for ANN, SVM, linear regression, and logistic regression. The figures therefore do not describe the performance of all five classifiers. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Previous studies explored the development of AI systems to identify diabetic neuropathy based on EHRs or corneal confocal microscopy images, with AUCs ranging from 0.83 to 0.95.

    “For neuropathy screening, previous studies explored the development of AI systems to identify diabetic neuropathy based on EHRs116 or corneal confocal microscopy images,117,118,119 with AUCs ranging from 0.83 to 0.95.”

    From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [116]

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Research works printed but not linked: 29

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Authors
Metsker, O., Magoev, K., Yakovlev, A., Yanishevskiy, S., Kopanitsa, G., Kovalchuk, S., & Krzhizhanovskaya, V. V.
Affiliations
  • Federal Almazov North-West Medical Research Centre (RU)
  • ITMO University (RU)
Corresponding author
Kopanitsa, Georgy
ORCID
Venue
BMC Medical Informatics and Decision Making
Publisher
Springer Science and Business Media LLC
Published
2020-08-24
Volume
20
Issue
1
Pages
201-201
ISSN
1472-6947 (ISSN-L)
DOI
10.1186/s12911-020-01215-w
PMC
PMC7444272
OpenAlex
W3080967847
Cited by
35
References
4
Citations by year
2026: 2 · 2025: 2 · 2024: 8 · 2023: 8 · 2022: 7 · 2021: 8
FWCI
5.05
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UvA-DARE (University of Amsterdam) (publishedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Russian scientific fund (19-11-00326)
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 › Neurology › Peripheral Neuropathies and Disorders
Keywords
diabetic polyneuropathy, risk factors, diabetes mellitus, electronic health records, laboratory findings, retinopathy, kidney disease, neutrophil count, glycemic control, predictive modeling, neural network classifier, random forest, decision support
MeSH terms
Machine Learning, Adult, Aged, Diabetic Neuropathies, Female, Humans, Male, Middle Aged, Prospective Studies, Risk Factors, Neural Networks, Computer

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