Deep learning for diabetes: a systematic review
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …
worldwide. Aiming to improve the treatment of people with diabetes, digital health has been …
Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction–a systematic literature review
Background and aim Hypoglycaemia prediction play an important role in diabetes
management being able to reduce the number of dangerous situations. Thus, it is relevant to …
management being able to reduce the number of dangerous situations. Thus, it is relevant to …
Do we really need deep learning models for time series forecasting?
Time series forecasting is a crucial task in machine learning, as it has a wide range of
applications including but not limited to forecasting electricity consumption, traffic, and air …
applications including but not limited to forecasting electricity consumption, traffic, and air …
Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction
Background Blood glucose (BG) management is crucial for type-1 diabetes patients
resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent …
resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent …
Exploring interpretable LSTM neural networks over multi-variable data
For recurrent neural networks trained on time series with target and exogenous variables, in
addition to accurate prediction, it is also desired to provide interpretable insights into the …
addition to accurate prediction, it is also desired to provide interpretable insights into the …
Shape and time distortion loss for training deep time series forecasting models
This paper addresses the problem of time series forecasting for non-stationary signals and
multiple future steps prediction. To handle this challenging task, we introduce DILATE …
multiple future steps prediction. To handle this challenging task, we introduce DILATE …
Benchmarking machine learning algorithms on blood glucose prediction for type I diabetes in comparison with classical time-series models
Objective: This paper aims to compare the performance of several commonly known
machine-learning (ML) models versus a classic Autoregression with Exogenous inputs …
machine-learning (ML) models versus a classic Autoregression with Exogenous inputs …
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
OA Montesinos-López… - G3: Genes, genomes …, 2018 - academic.oup.com
Multi-trait and multi-environment data are common in animal and plant breeding programs.
However, what is lacking are more powerful statistical models that can exploit the correlation …
However, what is lacking are more powerful statistical models that can exploit the correlation …
Chinese diabetes datasets for data-driven machine learning
Q Zhao, J Zhu, X Shen, C Lin, Y Zhang, Y Liang, B Cao… - Scientific Data, 2023 - nature.com
Data of the diabetes mellitus patients is essential in the study of diabetes management,
especially when employing the data-driven machine learning methods into the …
especially when employing the data-driven machine learning methods into the …
Ensemble blood glucose prediction in diabetes mellitus: A review
Considering the complexity of blood glucose dynamics, the adoption of a single model to
predict blood glucose level does not always capture the inter-and intra-patients' context …
predict blood glucose level does not always capture the inter-and intra-patients' context …