Showing papers in "Informatics in Medicine Unlocked in 2021"

TL;DR: In this paper, a convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19.

TL;DR: A review of recent reports on ML algorithms used in relation to COVID-19 can be found in this paper, where the authors focus on the potential of ML for two main applications: diagnosis of COVID19 and prediction of mortality risk and severity, using readily available clinical and laboratory data.

TL;DR: In this article, a wearable sensor placed on the body is connected to edge node in IoT cloud where the data is processed and analyzed to define the state of health condition, and the next layer is used to store the information in the cloud database for preventive measures, alerts, and immediate actions.

TL;DR: Wang et al. as discussed by the authors proposed a 3D U-Net-based segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases.

TL;DR: This work is the first work to condition a pre-trained transformer on visual and semantic features to generate medical reports and to include semantic similarity metrics in the quantitative analysis of the generated reports.

TL;DR: Major deep learning applications for IoT in health care and medical sciences are presented through analyzing the related works, and several potential issues such as privacy, QoS optimization, and deployment indicate the pivotal part of deep learning.

TL;DR: A new deep learning model is trained to accurately classify wheat diseases in 10 classes and has a high testing accuracy, which gives an improvement of 7.01% and 15.92% for the accuracy metric over the other two popular deep learning models – VGG16 and RESNET50, respectively.

TL;DR: This paper proposes a new method for the segmentation of blood vessels in retinal photographs, based on classical edge detection filters and artificial neural networks, which is a suitable tool for automated retinal image analysis.

TL;DR: This research focuses on developing robust cost-sensitive classifiers by modifying the objective functions of some well-known algorithms, such as logistic regression, decision tree, extreme gradient boosting, and random forest, which are then used to efficiently predict medical diagnosis.

TL;DR: In this paper, the authors focused on the detection of various chemical compounds in the exhaled breath and discussed the biomarkers used for clinical examination and diagnosis of the inhaled breath.

TL;DR: This paper will focus on the prediction of Coronary Heart Disease using a risk factor approach using K-Nearest Neighbors, Binary Logistic Classification, and Naive Bayes techniques with a cross-comparative study on the ‘Cardiovascular Disease Dataset’.

TL;DR: The results reveal that the proposed novel optimized algorithm can provide an effective healthcare monitoring system for the early prediction of heart disease.

TL;DR: In this article, the authors proposed a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models.

TL;DR: In this article, a wide range of bio-peptides and then conducted molecular docking analyses to investigate their binding affinity for the inhibition of these proteins, after obtaining the best bio peptides with the highest affinity scores, they were examined for further study and then manipulated to eliminate their side effects.

TL;DR: In this paper, an overview of current deep learning methods, starting from the most straightforward concept but accompanied by the mathematical models that are behind the functionality of this type of intelligence, is presented.

TL;DR: The proposed weighted average ensemble learning-based model to classify seven types of skin lesions performed better than other existing systems and can support dermatologists for diagnosis.

TL;DR: In this article, a peptide-based multi-epitope vaccine (MEV) against SARS-CoV-2 has been proposed, and the docked complexes during the simulation revealed a strong and stable binding interactions of MEV with human and mice toll-like receptors (TLR), TLR3 and TLR4.

TL;DR: In this paper, a machine learning-based model for the classification of malaria incidence using climate variability across six countries of Sub-Saharan Africa over a period of twenty-eight years was proposed.

TL;DR: This study provides a summary of AI-related research in healthcare, it discusses the challenges and proposes open research questions for further research, including robotics, a CAD, and AI-based personalized approach.

TL;DR: In this paper, the authors overview the recent applications of machine learning techniques contributing to prevention, diagnosis, monitoring, and treatment of coronavirus disease (SARS-CoV-2), and a progressive investigation of the recent publications up to November 2020, related to AI approaches towards managing the challenges of COVID-19 infection was made.

TL;DR: An insight is provided into the gradual evolution of deep learning in detecting prostate cancer and Gleason grading and a comprehensive, synthesized overview of the current state and existing methodological approaches as well as unique insights in prostate cancer detection using deep learning.

TL;DR: In this article, a model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) was proposed to predict COVID-19 confirmed and death cases.

TL;DR: In this paper, the authors proposed a system for the monitoring and communication of health parameters of a team of mountaineers while traversing steep slopes and steering through rugged terrain without the need for WiFi connectivity.

TL;DR: In this article, the authors evaluated the effectiveness of VR-based exercise therapy to highlight areas for future studies in rehabilitation and highlighted the potential effectiveness of virtual reality based exercise therapy for the improvement of rehabilitation outcomes.

TL;DR: This paper proposes an ensemble method-based multilayer dynamic system (MLDS) that can improve its current knowledge in every layer and has been compared to five other models, indicating that the proposed model can effectively predict cardiovascular disease.

TL;DR: In this article, the authors used the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data.

TL;DR: An ensemble learning method to classify brain tumors or neoplasms and auto-immune disease lesion using magnetic resonance imaging of brain tumors and multiple sclerosis patients and may infer an unprecedented step for detecting the presence of lesions coexisting with the tumors in neuro-medicine diagnosis.

TL;DR: A new classification network, Crack-Sensitive Convolutional Neural Network (CrackNet), which is sensitive to fracture lines is presented, which is better than other two-stage systems to detect fracture.

TL;DR: In this paper, the authors investigated a drug repurposing strategy aiming to screen compatible inhibitors of FDA-approved drugs against viral entry receptors (ACE2 and CD147) and integral enzyme of the viral polymerase (RdRp).

TL;DR: Results demonstrate the benefits of combining fog and cloud computing services to achieve higher network bandwidth reliability, a higher level of operation, and a shorter response time while generating real-time notifications, as compared to an existing cloud-only model.