Expert Systems with Applications

Closed-loop insulin delivery systems have proven effective in regulating blood glucose (BG) concentration, thereby reducing the burden of self-care in the management of type 1 and type 2 diabetes mellitus. However, the prevalence of unexpected disturbances resulting from oral glucose intake represents a considerable challenge to the full automation of these systems. Here, we propose an actor-critic reinforcement learning (RL) framework implemented within environments governed by compartmental ordinary differential equation models of the glucose-insulin-glucagon-incretins dynamics. This approach was employed to optimize automated insulin delivery in virtual patients with type 1 and type 2 diabetes mellitus, under scenarios involving unforeseen BG disturbances. The resulting optimal RL policies were tested in silico on virtual patients subjected to three unannounced glucose disturbances over the course of a day. The findings demonstrated that optimal RL policies could sustain the BG a significantly higher percentage of time within the normoglycemic range and a significantly lower percentage of time below the normoglycemic range in comparison to either continuous or discrete proportional-integral-derivative control algorithms. These results set the basis for developing new approaches to optimizing automated dosing regimens for chronic disease management. Author SummaryManaging diabetes requires constant attention to glucose levels and the corresponding adjustment of insulin doses, which can be demanding for insulin-dependent patients. Semi-automated systems, frequently referred to as "artificial pancreas", aim to mitigate this burden by adjusting insulin delivery based on glucose levels obtained from a continuous glucose sensor. However, these systems underperform in scenarios involving unexpected glucose disturbances, such as those triggered by the omission of meal announcing within the system interface. In the present study, we developed a computer-based learning approach to identify deep network-based functions that determine the appropriate insulin doses to regulate glucose in real time and mitigate unannounced disturbances. This approach has been demonstrated to be effective in the management of Type 1 and Type 2 diabetes and it does not require any additional input other than the continuous glucose measurements. The findings of our study demonstrate that our functions were able to maintain glucose levels within a healthy range for a longer period of time than the standard functions, while also reducing the risk of threating low glucose levels. This research contributes to the development of fully automated insulin delivery systems that are capable of adapting to real-life situations of individuals living with diabetes.

The purpose of this paper is to present a detailed investigation of the advantages of employing GraphLIME (Local Interpretable Model Explanations for Graph Neural Networks) for the trustworthy prediction of diabetes mellitus. Our pursuit involves identifying the strengths of GraphLIME combined with the attention-mechanism over the standard coupling of deep learning neural networks with the original LIME method. The system build this way, provided us a proficient method for extracting the most relevant features and applying the attention mechanism exclusively to those features. We have closely monitored the performance metrics of the two approaches and conducted a comparative analysis. Leveraging attention mechanisms, we have achieved an accuracy of 92.6% for the addressed problem. The models performance is meticulously demonstrated throughout the study, and the results are furthermore evaluated using the Receiver Operating Characteristic (ROC) curve. By implementing this technique on a dataset of 768 patients diagnosed with or without diabetes mellitus, we have successfully boosted the models performance by over 18%.

Diabetes Mellitus (DM) is a metabolic disorder characterized by hyperglycemia, with type 1 characterized as an autoimmune destruction of pancreatic beta cells and type 2 characterized by insulin resistance with progressive beta cell dysfunction. This study applied an existing binary classification algorithm (ALTARN) to accurately predict DM. ALTARN, as a tabular attention residual neural network, uses residual connection to find complex patterns present in tabular columns. We achieved an average training accuracy of 75.22%. Furthermore, a robust set of validation metrics was obtained via five-fold stratified cross-validation, yielding an average accuracy of 74.61%, an average precision of 72.36%, a mean recall of 79.69%, and a mean F1 score of 75.83%.

The demand for Continuous Glucose Monitoring systems is increasing among type 1 diabetic patients. Some companies are trying to improve the monitoring and the usability of these systems. One example is Abbott FreeStyle Libre, which provides a new concept of glucose monitoring called Flash Glucose Monitoring which is more affordable and does not need calibration. The increasing demand for these devices means an opportunity for data and computer scientists, who can contribute to the development of decision-making support systems based on the data collected from the devices. Type 1 diabetic patients that use FreeStyle Libre may enter the number of insulin and carbohydrates units that they are going to take before a meal. Using both the entered data and the blood glucose values collected by the device automatically, the application presented in this paper generates a report of the patients glucose patterns. In addition, it provides a web application that allows the user to upload the data obtained from the device and download the report on his computer or smartphone. The application uses decision trees to detect the patterns and entails a starting point in the creation of ensemble models with more predictive power, also based on decision trees. Furthermore, the methodology makes a segmentation of the data set in blocks, determined by the different meals done throughout the day, adding more information to the set of variables used to train the model. As a result, the application can discover repetitive patterns in the daily life of the patient, which can help to take early preventive measures for risk situations in a period close to the next meal.

This paper explores the use of machine learning (ML) for predictive modeling on clinical data from the EMory BrEast imaging Dataset (EMBED) [1] with a focus on ML model fairness analysis. The aim of this study is to develop and evaluate fair machine learning models that can accurately predict breast cancer risk. We trained and tested various machine-learning models. Our findings show that machine learning can be effective for predicting breast cancer risk or diagnosing breast cancer, and that fairness considerations are crucial in the development of such models. Overall, our study highlights the potential of machine learning for clinical applications while emphasizing the need for ethical and fair practices in this field.

BackgroundPatients with type 2 diabetes mellitus (T2DM) who have severe hypoglycemia (SH) poses a considerable risk of long-term death, demanding urgent medical attention. Accurate prediction of SH remains challenging due to its multifactorial nature, contributed from factors such as medications, lifestyle choices, and metabolic measurements. MethodIn this study, we propose a systematic approach to improve the robustness and accuracy of SH predictions using machine learning models, guided by clinical feature selection. Our focus is on developing one-year SH prediction models using both semi-supervised learning and supervised learning algorithms. Utilizing the clinical trial, namely Action to Control Cardiovascular Risk in Diabetes, which involves electronic health records for over 10,000 individuals, we specifically investigate adults with T2DM who are at an increased risk of cardiovascular complications. ResultsOur results indicate that the application of a multi-view co-training method, incorporating the random forest algorithm, improves the specificity of SH prediction, while the same setup with Naive Bayes replacing random forest demonstrates better sensitivity. Our framework also provides interpretability of machine learning (XAI) models by identifying key predictors for hypoglycemia, including fast plasma glucose, hemoglobin A1c, general diabetes education, and NPH or L insulins. ConclusionBy enhancing prediction accuracy and identifying crucial predictive features, our study contributes to advancing the understanding and management of hypoglycemia in this population.

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

Globally the devastating consequence of COVID-19 or Severe Acute Respiratory Syndrome-Coronavirus (SARS-CoV-2) has posed danger on the life of living beings. Doctors and scientists throughout the world are working day and night to combat the proliferation or transmission of this deadly disease in terms of technology, finances, data repositories, protective equipment, and many other services. Rapid and efficient detection of COVID-19 reduces the rate of spreading this deadly disease and early treatment improve the recovery rate. In this paper, we proposed a new framework to exploit powerful features extracted from the autoencoder and Gray Level Co-occurence Matrix (GLCM), combined with random forest algorithm for the efficient and fast detection of COVID-19 using computed tomographic images. The models performance is evident from its 97.78% accuracy, 96.78% recall, and 98.77% specificity.

Coronavirus is a global emergency as of May 2021. If not acted upon by drugs at the right time, coronavirus may result in the death of individuals. Hence early diagnosis is very important along the progress of the disease. This paper focuses on coronavirus detection using x-ray images, for automating the diagnosis pipeline using convolutional neural networks and transfer learning. This could be deployed in places where radiologists are not easily available in order to detect the disease at very early stages. In this study we propose our deep learning architecture for the classification task, which is trained with modified images, through multiple steps of preprocessing. Our classification method uses convolutional neural networks and transfer learning architecture for classifying the images. Our findings yield an accuracy value of 91.03%, precision of 89.76 %, recall value of 96.67% and F1 score of 93.09%.

Tuberculosis is an infectious disease that is leadingto the death of millions of people across the world. The mortalityrate of this disease is high in patients suffering from immuno-compromised disorders. The early diagnosis of this disease cansave lives and can avoid further complications. But the diagnosisof TB is a very complex task. The standard diagnostic tests stillrely on traditional procedures developed in the last century. Theseprocedures are slow and expensive. So this paper presents anautomatic approach for the diagnosis of TB from posteroanteriorchest x-rays. This is a two-step approach, where in the first stepthe lung regions are segmented from the chest x-rays using thegraph cut method, and then in the second step the transfer learn-ing of VGG16 combined with Bi-directional LSTM is used forextracting high-level discriminative features from the segmentedlung regions and then classification is performed using a fullyconnected layer. The proposed model is evaluated using data fromtwo publicly available databases namely Montgomery Countryset and Schezien set. The proposed model achieved accuracy andsensitivity of 97.76%, 97.01%and 96.42%, 94.11%on Schezienand Montgomery county datasets. This model enhanced thediagnostic accuracy of TB by 0.7%and 11.68%on Schezien andMontgomery county datasets.

In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network to achieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performance of VGG, ResNet, Inception ResNet v2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline[16] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset [25]. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline[31] and achieves 91.6% on ISIC-2017 dataset [2]. The code is publicly available at github1.

The novel coronavirus disease (COVID-19) and pandemic has taken the world by surprise and simultaneously challenged the health infrastructure of every country. Governments have resorted to draconian measures to contain the spread of the disease despite its devastating effect on their economies and education. Tracking the novel coronavirus 2019 disease remains vital as it influences the executive decisions needed to tighten or ease restrictions meant to curb the pandemic. One-Dimensional (1D) Convolution Neural Networks (CNN) have been used classify and predict several time-series and sequence data. Here 1D-CNN is applied to the time-series data of confirmed COVID-19 cases for all reporting countries and territories. The model performance was 90.5% accurate. The model was used to develop an automated AI tracker web app (AI Country Monitor) and is hosted on https://aicountrymonitor.org. This article also presents a novel concept of pandemic response curves based on cumulative confirmed cases that can be use to classify the stage of a country or reporting territory. It is our firm believe that this Artificial Intelligence COVID-19 tracker can be extended to other domains such as the monitoring/tracking of Sustainable Development Goals (SDGs) in addition to monitoring and tracking pandemics.

AO_SCPLOWBSTRACTC_SCPLOWDynamic distribution shifts caused by evolving diseases and demographic changes require domain-incremental adaptation of clinical deep learning models. However, this process of adaptation is often accompanied by catastrophic forgetting, and even the most sophisticated methods are not good enough for clinical applications. This paper studies incremental learning from the perspective of mode connections, that is, the low-loss paths connecting the minimisers of neural architectures (modes or trained weights) in the parameter space. The paper argues for learning the low-loss paths originating from an existing mode and exploring the learned paths to find an acceptable mode for the new domain. The learned paths, and hence the new domain mode, are a function of the existing mode. As a result, unlike traditional incremental learning, the proposed approach is able to exploit information from a deployed model without changing its weights. Pre-COVID and COVID-19 data collected in Oxford University hospitals are used as a case study to demonstrate the need for domain-incremental learning and the advantages of the proposed approach.

Deep learning models have proven the potential to aid professionals with medical image analysis, including many image classification tasks. However, the scarcity of data in medical imaging poses a significant challenge, as the limited availability of diverse and comprehensive datasets hinders the development and evaluation of accurate and robust imaging algorithms and models. Few-shot learning approaches have emerged as a potential solution to address this issue. In this research, we propose to deploy the Generalized Metric Learning Model for Few-Shot X-ray Image Classification. The model comprises a feature extractor to embed images into a lower-dimensional space and a distance-based classifier for label assignment based on the relative distance of these embeddings. We extensively evaluate the model using various pre-trained convolutional neural networks (CNNs) and vision transformers (ViTs) as feature extractors. We also assess the performance of the commonly used distance-based classifiers in several few-shot settings. Finally, we analyze the potential to adapt the feature encoders to the medical domain with both supervised and self-supervised frameworks. Our model achieves 0.689 AUROC in 2-way 5-shot COVID-19 recognition task when combined with REMEDIS (Robust and Efficient Medical Imaging with Self-supervision) domain-adapted model as feature extractor, and 0.802 AUROC in 2-way 5-shot tuberculosis recognition task with domain-adapted DenseNet-121 model. Moreover, the simplicity and flexibility of our approach allows for easy improvement in the feature, either by incorporating other few-shot methods or new, powerful architectures into the pipeline.

Although there is no universal definition for texture, the concept in various forms is nevertheless widely used and a key element of visual perception to analyze images in different fields. The present works main idea relies on the assumption that there exist representative samples, which we refer to as references as well, i.e., "good or bad" samples that represent a given dataset investigated in a particular data analysis problem. These representative samples need to be accounted for when designing predictive models with the aim of improving their performance. In particular, based on a selected subset of texture gray-level co-occurrence matrices (GLCMs) from the training cohort, we propose new representative spatial texture features, which we incorporate into a supervised image classification pipeline. The pipeline relies on the support vector machine (SVM) algorithm along with Bayesian optimization and the Wasserstein metric from optimal mass transport (OMT) theory. The selection of the best, "good and bad," GLCM references is considered for each classification label and performed during the training phase of the SVM classifier using a Bayesian optimizer. We assume that sample fitness is defined based on closeness (in the sense of the Wasserstein metric) and high correlation (Spearmans rank sense) with other samples in the same class. Moreover, the newly defined spatial texture features consist of the Wasserstein distance between the optimally selected references and the remaining samples. We assessed the performance of the proposed classification pipeline in diagnosing the corona virus disease 2019 (COVID-19) from computed tomographic (CT) images.

Leptin is a fat cell-derived hormone involved in satiety and body weight regulation. It also plays a critical regulatory role in the insulin-glucose regulatory system by modulating glucose metabolism and energy homeostasis. However, existing insulin-glucose models often fail to consider the impact of body weight indicators mainly body mass index (BMI) and plasma leptin. To address this limitation, we propose augmenting the ordinary differential equations (ODE) of the Oral Minimal Model (OMM) with an additional equation, incorporating leptin as well as supplementary terms and parameters. By estimating the model parameters, the model behaviour is aligned with the observed data of glucose, insulin and leptin for individuals with type 2 diabetes mellitus (T2DM). Based on model behaviour, revised indices formulated from Oral Glucose Tolerance Test (OGTT) data by including BMI and fasting leptin values are found to have a better correlation with existing indices. Additionally, parameter sensitivity analysis is performed to investigate the influence of the model parameters on the observed variables. Validation of the augmented model with clinical data (without leptin) demonstrates a superior fit to glucose and insulin data compared to the base model. This model emphasizes the intricate associations between leptin, glucose, and insulin concentrations with a potential for developing targeted interventions and therapies for T2DM. Notably, this manuscript introduces the first ODE-based model that incorporates leptin and BMI in the insulin-glucose pathway.

Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies.

The novel coronavirus (COVID-19) pandemic is pressurizing the healthcare systems across the globe and few of them are on the verge of failing. The detection of this virus as early as possible will help in contaminating the spread of it as the virus is mutating itself as fast as possible and currently there are about 4,300 strains of the virus according to the reports. Clinical studies have shown that most of the COVID-19 patients suffer from a lung infection similar to influenza. So, it is possible to diagnose lung infection using imaging techniques. Although a chest computed tomography (CT) scan has been shown to be an effective imaging technique for lung-related disease diagnosis, chest X-ray is more widely available across the hospitals due to its considerably lower cost and faster imaging time than CT scan. The advancements in the area of machine learning and pattern recognition has resulted in intelligent systems that analyze CT Scans or X-ray images and classify between pneumonia and normal patients. This paper proposes KE Sieve Neural Network architecture, which helps in the rapid diagnosis of COVID-19 using chest X-ray images. This architecture is achieving an accuracy of 98.49%. This noninvasive prediction method can assist the doctors in this pandemic and reduce the stress on health care systems.

Medical image segmentation (MIS) is an important task in medical image processing. Unfortunately, there is not a out-of-the-box python package for the evaluation metrics of MIS. Therefore, we developed seg-metrics, an open-source Python package for MIS model evaluation. Unlike existing packages, seg-metrics offers user-friendly interfaces for various overlap-based and distance-based metrics, providing a comprehensive solution. seg-metrics supports multiple file formats and is easily installable through the Python Package Index (PyPI). With a focus on speed and convenience, seg-metrics stands as a valuable tool for efficient MIS model assessment.