Machine Learning-Based Predictive Modeling of Complications of Chronic Diabetes.

Derevitskii, Ilia V.; Kovalchuk, Sergey V.

doi:10.1016/j.procs.2020.11.029

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…The advantage of using the aforementioned algorithms is obtaining intrinsic relationships between several predictors used simultaneously [215][216][217]. To date, several algorithms have been developed to assess associated cardiovascular risk [218][219][220], one such example being the one proposed by Jamthikar et al [221] which, using carotid vascular Doppler assessment and the presence of traditional cardiovascular risk factors, can assess associated cardiovascular risk with superior accuracy to traditional methods of calculation. Based on the socio-economic importance of the complications associated with DFS, artificial intelligence algorithms have been developed to screen diabetic patients to identify risk factors for the development of ulcers using different optical sensors [222][223][224][225].…”

Section: The Role Of Artificial Intelligence In Assessing Cvd Risk In Dmmentioning

confidence: 99%

Old and Novel Predictors for Cardiovascular Risk in Diabetic Foot Syndrome—A Narrative Review

et al. 2023

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Diabetic foot syndrome (DFS) is a complication associated with diabetes that has a strong negative impact, both medically and socio-economically. Recent epidemiological data show that one in six patients with diabetes will develop an ulcer in their lifetime. Vascular complications associated with diabetic foot have multiple prognostic implications in addition to limiting functional status and leading to decreased quality of life for these patients. We searched the electronic databases of PubMed, MEDLINE and EMBASE for studies that evaluated the role of DFS as a cardiovascular risk factor through the pathophysiological mechanisms involved, in particular the inflammatory ones and the associated metabolic changes. In the era of evidence-based medicine, the management of these cases in multidisciplinary teams of “cardio-diabetologists” prevents the occurrence of long-term disabling complications and has prognostic value for cardiovascular morbidity and mortality among diabetic patients. Identifying artificial-intelligence-based cardiovascular risk prediction models or conducting extensive clinical trials on gene therapy or potential therapeutic targets promoted by in vitro studies represent future research directions with a modulating role on the risk of morbidity and mortality in patients with DFS.

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Section: The Role Of Artificial Intelligence In Assessing Cvd Risk In Dmmentioning

confidence: 99%

Old and Novel Predictors for Cardiovascular Risk in Diabetic Foot Syndrome—A Narrative Review

et al. 2023

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“…In addition to CVD and diabetes" the presence of such comorbidities in patients profoundly impacts the nonlinear dynamics [113]. As a result, the importance of DL is growing in identifying moderate and high-risk patients with CVD/stroke risk [114][115][116]. Considering this, for superior CVD/stroke risk, an improved set of biomarkers for DFI severity is needed.…”

Section: Ml/dl-based Cvd/stroke Risk Assessment In Diabetics Foot Ulc...mentioning

confidence: 99%

“…Derevitskii et al [115] proposed that DM is among the most frequent forms of diabetes, also known as chronic diabetes. This particular form of diabetes is among the healthcare industry's most pressing concerns today.…”

Section: Benchmarkingmentioning

confidence: 99%

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

Khanna¹,

Maindarkar

Viswanathan

et al. 2022

JCM

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A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

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“…As a quantitative tool for assessing risks and benefits, it provides healthcare professionals with visual and accurate data information, and its use is becoming increasingly common [9,10]. Through a literature review, we found that most of the studies that established prediction models for T2DM in the Chinese region were single predictions of T2DM or some type of complications [11][12][13][14][15], and few studies have been reported on the pattern and prediction of T2DM complications. Machine learning(ML), a branch of artificial intelligence, plays a crucial role in various applications by employing diverse algorithms and statistical models to enable computer systems to learn from data and extract meaningful patterns.…”

Section: Introductionmentioning

confidence: 99%

Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

et al. 2023

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In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell’s concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744−0.792) and 0.745 (95% CI, 0.669−0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30–54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.

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Machine Learning-Based Predictive Modeling of Complications of Chronic Diabetes.

Cited by 10 publications

References 13 publications

Old and Novel Predictors for Cardiovascular Risk in Diabetic Foot Syndrome—A Narrative Review

Old and Novel Predictors for Cardiovascular Risk in Diabetic Foot Syndrome—A Narrative Review

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study

Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

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