Machine learning has become a popular tool for learning models of complex dynamics from biomedical data. In Type 1 Diabetes (T1D) management, these models are increasingly been integrated in decision support systems (DSS) to forecast glucose levels and provide preventive therapeutic suggestions, like corrective insulin boluses (CIB), accordingly. Typically, models are chosen based on their prediction accuracy. However, since patient safety is a concern in this application, the algorithm should also be physiologically sound and its outcome should be explainable. This paper aims to discuss the importance of using tools to interpret the output of black-box models in T1D management by presenting a case-of-study on the selection of the best prediction algorithm to integrate in a DSS for CIB suggestion. By retrospectively "replaying" real patient data, we show that two long-short term memory neural networks (LSTM) (named p-LSTM and np-LSTM) with similar prediction accuracy could lead to different therapeutic decisions. An analysis with SHAP-a tool for explaining black-box models' output-unambiguously shows that only p-LSTM learnt the physiological relationship between inputs and glucose prediction, and should therefore be preferred. This is verified by showing that, when embedded in the DSS, only p-LSTM can improve patients' glycemic control.
Intrusion Detection System (IDS) is one of the most important security tool for many security issues that are prevailing in today's cyber world. Intrusion Detection System is designed to scan the system applications and network traffic to detect suspicious activities and issue an alert if it is discovered. So many techniques are available in machine learning for intrusion detection. The main objective of this project is to apply machine learning algorithms to the data set and to compare and evaluate their performances. The proposed application has used the SVM (Support Vector Machine) and ANN (Artificial Neural Networks) Algorithms to detect the intrusion rates. Each algorithm is used to detect whether the requested data is authorized or contains any anomalies. While IDS scans the requested data if it finds any malicious information it drops that request. These algorithms have used Correlation-Based and Chi-Squared Based feature selection algorithms to reduce the dataset by eliminating the useless data. The preprocessed dataset is trained and tested with the models to obtain the prominent results, which leads to increasing the prediction accuracy. The NSL KDD dataset has been used for the experimentation. Finally, an accuracy of about 48% has been achieved by the SVM algorithm and 97% has been achieved by ANN algorithm. Henceforth, ANN model is working better than the SVM on this dataset.
Post-prandial hypoglycemia occurs 2-5 hours after food intake, in not only insulin-treated patients with diabetes but also other metabolic disorders. For example, postprandial hypoglycemia is an increasingly recognized late metabolic complication of bariatric surgery (also known as PBH), particularly gastric bypass. Underlying mechanisms remain incompletely understood to date. Besides excessive insulin exposure, impaired counter-regulation may be a further pathophysiological feature. To test this hypothesis, we need standardized postprandial hypoglycemic clamp procedures in affected and unaffected individuals allowing to reach identical predefined postprandial hypoglycemic trajectories. Generally, in these experiments, clinical investigators manually adjust glucose infusion rate (GIR) to clamp blood glucose (BG) to a target hypoglycemic value. Nevertheless, reaching the desired target by manual adjustment may be challenging and possible glycemic undershoots when approaching hypoglycemia can be a safety concern for patients. In this study, we developed a PID algorithm to assist clinical investigators in adjusting GIR to reach the predefined trajectory and hypoglycemic target. The algorithm is developed in a manual mode to permit the clinical investigator to interfere. We test the controller in silico by simulating glucose-insulin dynamics in PBH and healthy nonsurgical individuals. Different scenarios are designed to test the robustness of the algorithm to different sources of variability and to errors, e.g. outliers in the BG measurements, sampling delays or missed measurements. The results prove that the PID algorithm is capable of accurately and safely reaching the target BG level, on both healthy and PBH subjects, with a median deviation from reference of 2.8% and 2.4% respectively.Clinical relevance— This control algorithm enables standardized, accurate and safe postprandial hypoglycemic clamps, as evidenced in silico in PBH patients and controls.
Abstract Aims/hypothesis Post-bariatric hypoglycaemia is an increasingly recognised complication of bariatric surgery, manifesting particularly after Roux-en-Y gastric bypass. While hyperinsulinaemia is an established pathophysiological feature, the role of counter-regulation remains unclear. We aimed to assess counter-regulatory hormones and glucose fluxes during insulin-induced postprandial hypoglycaemia in patients with post-bariatric hypoglycaemia after Roux-en-Y gastric bypass vs surgical and non-surgical control individuals. Methods In this case–control study, 32 adults belonging to four groups with comparable age, sex and BMI (patients with post-bariatric hypoglycaemia, Roux-en-Y gastric bypass, sleeve gastrectomy and non-surgical control individuals) underwent a postprandial hypoglycaemic clamp in our clinical research unit to reach the glycaemic target of 2.5 mmol/l 150–170 min after ingesting 15 g of glucose. Glucose fluxes were assessed during the postprandial and hypoglycaemic period using a dual-tracer approach. The primary outcome was the incremental AUC of glucagon during hypoglycaemia. Catecholamines, cortisol, growth hormone, pancreatic polypeptide and endogenous glucose production were also analysed during hypoglycaemia. Results The rate of glucose appearance after oral administration, as well as the rates of total glucose appearance and glucose disappearance, were higher in both Roux-en-Y gastric bypass groups vs the non-surgical control group in the early postprandial period (all p <0.05). During hypoglycaemia, glucagon exposure was significantly lower in all surgical groups vs the non-surgical control group (all p <0.01). Pancreatic polypeptide levels were significantly lower in patients with post-bariatric hypoglycaemia vs the non-surgical control group (median [IQR]: 24.7 [10.9, 38.7] pmol/l vs 238.7 [186.3, 288.9] pmol/l) ( p =0.005). Other hormonal responses to hypoglycaemia and endogenous glucose production did not significantly differ between the groups. Conclusions/interpretation The glucagon response to insulin-induced postprandial hypoglycaemia is lower in post-bariatric surgery individuals compared with non-surgical control individuals, irrespective of the surgical modality. No significant differences were found between patients with post-bariatric hypoglycaemia and surgical control individuals, suggesting that impaired counter-regulation is not a root cause of post-bariatric hypoglycaemia. Trial registration ClinicalTrials.gov NCT04334161 Graphical abstract
People with type 1 diabetes (T1D) face the challenge of administering exogenous insulin to maintain blood glucose (BG) levels in a safe physiological range, so as to avoid (possibly severe) complications. By automatizing insulin infusion, the artificial pancreas (AP) assists patients in this challenge. While insulin can decrease BG, having another input inducing glucose increase could further improve BG control. Here, we develop a model predictive control (MPC) algorithm that, in addition to insulin infusion, also provides suggestions of carbohydrates (CHOs) as a second, glucose-increasing, control input. Since CHO consumption has to be manually actuated, great care is paid in limiting the extra burden that may be caused to patients. By resorting to a mixed logical-dynamical MPC formulation, CHO intake is designed to be sparse in time and quantized. The algorithm is validated on the UVa/Padua T1D simulator, a well-established large-scale model of T1D metabolism, accepted by Food and Drug Administration (FDA). Compared with an insulin-only MPC, the new algorithm ensures increased time spent in the safe physiological range in 75% of patients. The improvement is limited for those already well controlled by the state-of-art strategy but relevant for the others: the 25th percentile of this metric is increased from 74.75% to 79.06% in the population. This is achieved while simultaneously decreasing time spent in hypoglycemia (from 0.5% to 0.12% in median) and with limited manual interventions (2.86 per day in median).
Introduction & Objective: Prior studies do not identify if continuous glucose monitor (CGM) metrics at a critical gestational age (GA) can distinguish risk of adverse pregnancy outcomes (APOs). We evaluated 3rd trimester CGM metrics by GA and APO status in gravidas with type 1 diabetes (T1D). Methods: Dexcom G6 CGM data from singleton pregnancies with T1D (2018-2022) were retrospectively analyzed. Time in, above, and below range 63-140 mg/dL (TIR, TAR, TBR) and glycemic variability (CV) were computed in 2-week CGM intervals from 28°-396 weeksdays. APOs were hypertensive disorders of pregnancy (HDP), large for gestational age (LGA), NICU admission, neonatal hypoglycemia (NH), and respiratory distress syndrome (RDS). Linear mixed-effects models were fitted on CGM metrics with GA, APO status, and their interaction as fixed effects. Results: In 86 pregnancies (1st trimester mean HbA1c 6.1%, BMI 25 kg/m2), 71% had at least 1 APO. At 28° weeks, pregnancies with HDP, NICU, or RDS, had higher TAR (p<0.01) and lower TIR (p<0.05). TIR evolution across the 3rd trimester differed in the presence vs. absence of HDP (p<0.05; Figure), with greatest interaction at 28°-316 weeks. Evolution of all metrics differed in the presence vs. absence of RDS (p<0.05), with greatest interaction at 28°-336 weeks. Conclusion: The early 3rd trimester, a period of peak insulin resistance, is a critical window to optimize CGM metrics to mitigate APO risk. Disclosure S.A. Fisher: None. M.F. Villa Tamayo: Research Support; Dexcom, Inc. J. Pavan: Other Relationship; Dexcom, Inc. M. Moscoso-Vasquez: Research Support; Dexcom, Inc. Other Relationship; Dexcom, Inc. C. Fabris: Research Support; Novo Nordisk A/S. Other Relationship; Novo Nordisk A/S, Dexcom, Inc. N. Conboy: None. C.M. Niznik: None. L.D. Yee: None. M.A. Kohn: None. E. Kobayashi: None. A.R. Majithia: None. J. Huang: None. T. Tian: None. R. Aaron: None. D.C. Klonoff: Consultant; Afon Technology Ltd, Better Therapeutics, Inc, Glucotrack, LIfecare, Nevro Corp., Novo Nordisk, Samsung, Thirdwayv Inc.
To provide a preliminary evaluation of the accuracy and safety of Gluclas decision support system suggestions in a hypoglycaemic clamp study.This analysis was performed using data from 32 participants (four groups with different glucose-insulin regulation: post Roux-en-Y gastric bypass with and without postprandial hypoglycaemia syndrome, postsleeve gastrectomy and non-operated controls) undergoing Gluclas-assisted hypoglycaemic clamps (target: 2.5 mmol/L for 20 minutes at 150 minutes after oral glucose ingestion). Gluclas provided glucose infusion rate suggestions upon manual entry of blood glucose values (every 5 minutes), which were either followed or overruled by investigators after critical review. Accuracy and safety were evaluated by mean absolute error (MAE), mean absolute percentage error (MAPE), average glucose level, coefficient of variation (CV) and minimal glucose level during the 20-minute hypoglycaemic period.Investigators accepted 84% of suggestions, with a mean deviation of 30.33 mg/min. During the hypoglycaemic period, the MAE was 0.16 (0.12-0.24) (median [interquartile range]) mmol/L and the MAPE was 6.12% (4.80%-9.29%). CV was 4.90% (3.58%-7.27%), with 5% considered the threshold for sufficient quality. The minimal glucose level was 2.40 (2.30-2.50) mmol/L.Gluclas achieved sufficiently high accuracy with minimal safety risks in a population with differences in glucose-insulin dynamics, underscoring its applicability to various patient groups.
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