Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study)

Objective Machine learning involves the use of algorithms without explicit instructions. Of late, machine learning models have been widely applied for the prediction of type 2 diabetes. However, no evidence synthesis of the performance of these prediction models of type 2 diabetes is available. We aim to identify machine learning prediction models for type 2 diabetes in clinical and community care settings and determine their predictive performance. Methods The systematic review of English language machine learning predictive modeling studies in 12 databases will be conducted. Studies predicting type 2 diabetes in predefined clinical or community settings are eligible. Standard CHARMS and TRIPOD guidelines will guide data extraction. Methodological quality will be assessed using a predefined risk of bias assessment tool. The extent of validation will be categorized by Reilly–Evans levels. Primary outcomes include model performance metrics of discrimination ability, calibration, and classification accuracy. Secondary outcomes include candidate predictors, algorithms used, level of validation, and intended use of models. The random-effects meta-analysis of c-indices will be performed to evaluate discrimination abilities. The c-indices will be pooled per prediction model, per model type, and per algorithm. Publication bias will be assessed through funnel plots and regression tests. Sensitivity analysis will be conducted to estimate the effects of study quality and missing data on primary outcome. The sources of heterogeneity will be assessed through meta-regression. Subgroup analyses will be performed for primary outcomes. Ethics and dissemination No ethics approval is required, as no primary or personal data are collected. Findings will be disseminated through scientific sessions and peer-reviewed journals. PROSPERO registration number CRD42019130886

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Hypoglycemia Prediction Using Machine Learning Models for Patients With Type 2 Diabetes

Journal of Diabetes Science and Technology ◽

10.1177/1932296814554260 ◽

2014 ◽

Vol 9 (1) ◽

pp. 86-90 ◽

Cited By ~ 52

Author(s):

Bharath Sudharsan ◽

Malinda Peeples ◽

Mansur Shomali

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Learning Models ◽

Machine Learning Models

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Application of Machine Learning Models to Evaluate Hypoglycemia Risk in Type 2 Diabetes

Diabetes Therapy ◽

10.1007/s13300-020-00759-4 ◽

2020 ◽

Vol 11 (3) ◽

pp. 681-699 ◽

Cited By ~ 1

Author(s):

Luke Mueller ◽

Paulos Berhanu ◽

Jonathan Bouchard ◽

Veronica Alas ◽

Kenneth Elder ◽

...

Keyword(s):

Machine Learning ◽

Type 2 Diabetes ◽

Learning Models ◽

Machine Learning Models

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Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽

10.3390/e23010018 ◽

2020 ◽

Vol 23 (1) ◽

pp. 18

Author(s):

Pantelis Linardatos ◽

Vasilis Papastefanopoulos ◽

Sotiris Kotsiantis

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Black Box ◽

Learning Systems ◽

Model Complexity ◽

Learning Models ◽

New Methods ◽

Industrial Adoption ◽

Machine Learning Models ◽

The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

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Train Deep Learning Models using subsurface geological images datasets

10.5194/egusphere-egu21-6385 ◽

2021 ◽

Author(s):

Ramy Abdallah ◽

Clare E. Bond ◽

Robert W.H. Butler

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Learning Models ◽

Structural Interpretation ◽

Performance Accuracy ◽

Learning Techniques ◽

Machine Learning Models

<p>Machine learning is being presented as a new solution for a wide range of geoscience problems. Primarily machine learning has been used for 3D seismic data processing, seismic facies analysis and well log data correlation. The rapid development in technology with open-source artificial intelligence libraries and the accessibility of affordable computer graphics processing units (GPU) makes the application of machine learning in geosciences increasingly tractable. However, the application of artificial intelligence in structural interpretation workflows of subsurface datasets is still ambiguous. This study aims to use machine learning techniques to classify images of folds and fold-thrust structures. Here we show that convolutional neural networks (CNNs) as supervised deep learning techniques provide excellent algorithms to discriminate between geological image datasets. Four different datasets of images have been used to train and test the machine learning models. These four datasets are a seismic character dataset with five classes (faults, folds, salt, flat layers and basement), folds types with three classes (buckle, chevron and conjugate), fault types with three classes (normal, reverse and thrust) and fold-thrust geometries with three classes (fault bend fold, fault propagation fold and detachment fold). These image datasets are used to investigate three machine learning models. One Feedforward linear neural network model and two convolutional neural networks models (Convolution 2d layer transforms sequential model and Residual block model (ResNet with 9, 34, and 50 layers)). Validation and testing datasets forms a critical part of testing the model&#8217;s performance accuracy. The ResNet model records the highest performance accuracy score, of the machine learning models tested. Our CNN image classification model analysis provides a framework for applying machine learning to increase structural interpretation efficiency, and shows that CNN classification models can be applied effectively to geoscience problems. The study provides a starting point to apply unsupervised machine learning approaches to sub-surface structural interpretation workflows.</p>

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Recent Progress in Quantum Machine Learning

Limitations and Future Applications of Quantum Cryptography - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-6677-0.ch012 ◽

2021 ◽

pp. 232-256

Author(s):

Amandeep Singh Bhatia ◽

Renata Wong

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Quantum Computing ◽

Recent Progress ◽

Machine Learning Algorithms ◽

Learning Models ◽

Exciting Field ◽

Quantum Machine Learning ◽

The Subject ◽

Machine Learning Models

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

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