NIRCa: An artificial neural network-based insulin resistance calculator

Stawiski, Konrad; Pietrzak, Iwona; Młynarski, Wojciech; Fendler, Wojciech; Szadkowska, Agnieszka

doi:10.1111/pedi.12551

Cited by 12 publications

(25 citation statements)

References 16 publications

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“…Standard laboratory tests were collected from each participant, including HbA1c, vitamin D, lipid profile, and liver enzymes. The glucose disposal rate was estimated using neural network approximation as described previously [ 8 , 9 ].…”

Section: Methodsmentioning

confidence: 99%

Reduced Carbohydrate Diet Influence on Postprandial Glycemia—Results of a Short, CGM-Based, Interventional Study in Adolescents with Type 1 Diabetes

et al. 2022

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Therapy for type 1 diabetes (T1DM) focuses on maintaining optimal blood glucose levels, achieved with intensive insulin treatment, proper nutrition, and physical activity. The aim of this study was to investigate postprandial glycemic changes under low (30%) and standard (50%) carbohydrate diets in adolescents with T1DM. A single-center cross-over nutritional study was conducted, during which 26 adolescent patients provided 220 continuous glucose-monitored (CGM) meals data from the two consecutive 3-day nutritional plans. Overall, the 50% carbohydrate diet was associated with higher postprandial glucose variability in the small meals (afternoon snacks, second breakfast) and greater postprandial peaks for other meals (breakfast, dinner, supper). Nevertheless, after the adjustment of a patient’s individual clinical variables (age, Tanner classification, glucose disposal rate), we observed that mean postprandial glucose was higher for afternoon snacks and lower for suppers in the 30% carbohydrate diet. Although a 30% carbohydrate diet seems to offer better postprandial glycemia, it requires additional attention from the physician and patient when it comes to modifying daily carbohydrate intake. Increased fat/protein content and size of the main meal lead to a prolonged postprandial glycemic response, which may affect the insulin treatment and result in suboptimal glycemic control.

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Section: Methodsmentioning

confidence: 99%

Reduced Carbohydrate Diet Influence on Postprandial Glycemia—Results of a Short, CGM-Based, Interventional Study in Adolescents with Type 1 Diabetes

et al. 2022

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“…In addition, these models focused on DM or METS, states that are often the result of further IR progression, whereas our model on IR has the potential to stop disease onset at an earlier stage. Stawiski et al 35 applied neural network techniques to construct a predictive model for IR in children and achieved good predictive performance. However, in terms of the population to which it was adapted, their study mainly focused on children with T1DM, while our study faced a broader population covering all children aged 6–12 years.…”

Section: Discussionmentioning

confidence: 99%

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

Zhang¹,

Wan²

2022

DMSO

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Background: Due to the increasing insulin resistance (IR) in childhood, rates of diabetes and cardiovascular disease may rise in the future and seriously threaten the healthy development of children. Finding an easy way to predict IR in children can help pediatricians to identify these children in time and intervene appropriately, which is particularly important for practitioners in primary health care. Patients and Methods: Seventeen features from 503 children 6-12 years old were collected. We defined IR by HOMA-IR greater than 3.0, thus classifying children with IR and those without IR. Data were preprocessed by multivariate imputation and oversampling to resolve missing values and data imbalances; then, recursive feature elimination was applied to further select features of interest, and 5 machine learning methods -namely, logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and gradient boosting with categorical features support (CatBoost)-were used for model training. We tested the trained models on an external test set containing information from 133 children, from which performance metrics were extracted and the optimal model was selected. Results: After feature selection, the numbers of chosen features for the LR, SVM, RF, XGBoost, and CatBoost models were 6, 9, 10, 14, and 6, respectively. Among them, glucose, waist circumference, and age were chosen as predictors by most of the models. Finally, all 5 models achieved good performance on the external test set. Both XGBoost and CatBoost had the same AUC (0.85), which was highest among those of all models. Their accuracy, sensitivity, precision, and F1 scores were also close, but the specificity of XGBoost reached 0.79, which was significantly higher than that of CatBoost, so XGBoost was chosen as the optimal model. Conclusion:The model developed herein has a good predictive ability for IR in children 6-12 years old and can be clinically applied to help pediatricians identify children with IR in a simple and inexpensive way.

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“…Develop an enhanced reinforcement learning model for personalized insulin delivery and glucose con- Most studies (n=6) focused on the discovery of etiologic or prognostic biomarkers of T1DM [77,80,81,85,88,89], followed by studies aiming to predict hypoglycemia using noninvasive methods such as ECG or EEG signals, breath volatile organic compounds (n=5) [21,67,69,70,76], insulin bolus calculators for closed-loop glucose control (n=5) [66,[82][83][84]90], accurate prediction of glucose levels or hypoglycemia from continuous glucose monitor (CGM) data (n=4) [72,73,78,79], etiologic or risk factors for insulin resistance or T2DM (n=4) [71,74,75,86], and other goals such as long-term cardiovascular risk stratification [68], accurate carbohydrate counting via a smartphone app [18], prediction of cystic fibrosis-related DM from CGM signal [87], and the economic evaluation of diabetic retinopathy screening via AI versus standard care [91].…”

Section: Model-free Actorcritic Learning Algorithmmentioning

confidence: 99%

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

Zrubka,

Kertész,

Gulácsi

et al. 2024

J Med Internet Res

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Background Diabetes mellitus (DM) is a major health concern among children with the widespread adoption of advanced technologies. However, concerns are growing about the transparency, replicability, biasedness, and overall validity of artificial intelligence studies in medicine. Objective We aimed to systematically review the reporting quality of machine learning (ML) studies of pediatric DM using the Minimum Information About Clinical Artificial Intelligence Modelling (MI-CLAIM) checklist, a general reporting guideline for medical artificial intelligence studies. Methods We searched the PubMed and Web of Science databases from 2016 to 2020. Studies were included if the use of ML was reported in children with DM aged 2 to 18 years, including studies on complications, screening studies, and in silico samples. In studies following the ML workflow of training, validation, and testing of results, reporting quality was assessed via MI-CLAIM by consensus judgments of independent reviewer pairs. Positive answers to the 17 binary items regarding sufficient reporting were qualitatively summarized and counted as a proxy measure of reporting quality. The synthesis of results included testing the association of reporting quality with publication and data type, participants (human or in silico), research goals, level of code sharing, and the scientific field of publication (medical or engineering), as well as with expert judgments of clinical impact and reproducibility. Results After screening 1043 records, 28 studies were included. The sample size of the training cohort ranged from 5 to 561. Six studies featured only in silico patients. The reporting quality was low, with great variation among the 21 studies assessed using MI-CLAIM. The number of items with sufficient reporting ranged from 4 to 12 (mean 7.43, SD 2.62). The items on research questions and data characterization were reported adequately most often, whereas items on patient characteristics and model examination were reported adequately least often. The representativeness of the training and test cohorts to real-world settings and the adequacy of model performance evaluation were the most difficult to judge. Reporting quality improved over time (r=0.50; P=.02); it was higher than average in prognostic biomarker and risk factor studies (P=.04) and lower in noninvasive hypoglycemia detection studies (P=.006), higher in studies published in medical versus engineering journals (P=.004), and higher in studies sharing any code of the ML pipeline versus not sharing (P=.003). The association between expert judgments and MI-CLAIM ratings was not significant. Conclusions The reporting quality of ML studies in the pediatric population with DM was generally low. Important details for clinicians, such as patient characteristics; comparison with the state-of-the-art solution; and model examination for valid, unbiased, and robust results, were often the weak points of reporting. To assess their clinical utility, the reporting standards of ML studies must evolve, and algorithms for this challenging population must become more transparent and replicable.

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NIRCa: An artificial neural network-based insulin resistance calculator

Abstract: The developed GDR estimation model reliant on ANN allows for an optimized prediction of GDR for research and clinical purposes.

Cited by 12 publications

References 16 publications

Reduced Carbohydrate Diet Influence on Postprandial Glycemia—Results of a Short, CGM-Based, Interventional Study in Adolescents with Type 1 Diabetes

Reduced Carbohydrate Diet Influence on Postprandial Glycemia—Results of a Short, CGM-Based, Interventional Study in Adolescents with Type 1 Diabetes

Simple Method to Predict Insulin Resistance in Children Aged 6–12 Years by Using Machine Learning

The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review

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