Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes

D’Antoni, Federico; Petrosino, Lorenzo; Marchetti, Alessandro; Bacco, Luca; Pieralice, Silvia; Vollero, Luca; Pozzilli, Paolo; Piemonte, Vincenzo; Merone, Mario

doi:10.1109/access.2023.3237992

Cited by 7 publications

(2 citation statements)

References 63 publications

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“…If left untreated, Type 1 diabetes mellitus (T1D) can lead to severe complications. This study introduces a layered meta-learning strategy employing multi-expert systems to predict adverse events in T1D patients [11]. Numerous algorithms utilizing model predictive control and reinforcement learning (RL) have been introduced, with many necessitating prior knowledge of physiological systems, the mathematical structure of blood glucose dynamics, and multiple episodes (including failures) to train the RL policy network [12].…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

N. Laxmi

2023

IJRITCC

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This paper addresses the problem of predicting insulin dosage in diabetes patients using the PSO-LSTM, COA-LSTM, and LOA-LSTM algorithms. Accurate insulin dosage prediction is crucial in effectively managing Diabetes and maintaining blood glucose levels within the desired range. The study proposes a novel approach that combines particle swarm optimization (PSO) with the long short-term memory (LSTM) model. PSO is used to optimize the LSTM's parameters, enhancing its prediction capabilities specifically for insulin dosage. Additionally, two other techniques, COA-LSTM and LOA-LSTM, are introduced for comparison purposes. The algorithms utilize a dataset comprising relevant features such as past insulin dosages, blood glucose levels, carbohydrate intake, and physical activity. These features are fed into the PSO-LSTM, COA-LSTM, and LOA-LSTM models to predict the appropriate insulin dosage for future time points. The results demonstrate the effectiveness of the proposed PSO-LSTM algorithm in accurately predicting insulin dosage, surpassing the performance of COA-LSTM and LOA-LSTM. The PSO-LSTM model achieves a high level of accuracy, aiding in personalized and precise insulin administration for diabetes patients. By leveraging the power of PSO optimization and LSTM modeling, this research improves the accuracy and reliability of insulin dosage prediction. The findings highlight the potential of the PSO-LSTM algorithm as a valuable tool for healthcare professionals in optimizing diabetes management and enhancing patient outcomes.

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Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

N. Laxmi

2023

IJRITCC

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“…UK Biobank collection of accelerometer traces from 103712 was used for the T2D detection [ 19 ] The proposed model achieved F1-score of around 0.80 for positive class and 0.73 for negative class. Interested readers are referred to this article for a quick review on the existing ML models for controlling diabetes [ 20 , 21 ]. A summary of the ML based studies for diabetes detection is presented in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health

Alsadi,

Musleh,

Al-Absi

et al. 2024

BMC Med Inform Decis Mak

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Background Diabetes is a chronic condition that can result in many long-term physiological, metabolic, and neurological complications. Therefore, early detection of diabetes would help to determine a proper diagnosis and treatment plan. Methods In this study, we employed machine learning (ML) based case-control study on a diabetic cohort size of 1000 participants form Qatar Biobank to predict diabetes using clinical and bone health indicators from Dual Energy X-ray Absorptiometry (DXA) machines. ML models were utilized to distinguish diabetes groups from non-diabetes controls. Recursive feature elimination (RFE) was leveraged to identify a subset of features to improve the performance of model. SHAP based analysis was used for the importance of features and support the explainability of the proposed model. Results Ensemble based models XGboost and RF achieved over 84% accuracy for detecting diabetes. After applying RFE, we selected only 20 features which improved the model accuracy to 87.2%. From a clinical standpoint, higher HDL-Cholesterol and Neutrophil levels were observed in the diabetic group, along with lower vitamin B12 and testosterone levels. Lower sodium levels were found in diabetics, potentially stemming from clinical factors including specific medications, hormonal imbalances, unmanaged diabetes. We believe Dapagliflozin prescriptions in Qatar were associated with decreased Gamma Glutamyltransferase and Aspartate Aminotransferase enzyme levels, confirming prior research. We observed that bone area, bone mineral content, and bone mineral density were slightly lower in the Diabetes group across almost all body parts, but the difference against the control group was not statistically significant except in T12, troch and trunk area. No significant negative impact of diabetes progression on bone health was observed over a period of 5-15 yrs in the cohort. Conclusion This study recommends the inclusion of ML model which combines both DXA and clinical data for the early diagnosis of diabetes.

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Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics

Domanski,

Ray,

Lafata

et al. 2024

Biocybernetics and Biomedical Engineering

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Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes

Abstract: This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Ethics Committee of Campus Bio-Medico University of Rome (11/05/2021), and performed in line with the Declaration of Helsinki.

Cited by 7 publications

References 63 publications

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

An ensemble-based machine learning model for predicting type 2 diabetes and its effect on bone health

Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics

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