Evaluating the accuracy of lung-RADS score extraction from radiology reports: Manual entry versus natural language processing.

Introduction: Radiology scoring systems are critical to the success of lung cancer screening (LCS) programs, impacting patient care, adherence to follow-up, data management and reporting, and program evaluation. LungCT ScreeningReporting and Data System (Lung-RADS) is a structured radiology scoring system that provides recommendations for LCS follow-up that are utilized (a) in clinical care and (b) by LCS programs monitoring rates of adherence to follow-up. Thus, accurate reporting and reliable collection of Lung-RADS scores are fundamental components of LCS program evaluation and improvement.

An attention-based deep learning for acute lymphoblastic leukemia classification.

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data.

ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.

Background: Despite the significance and prevalence of acute respiratory distress syndrome (ARDS), its detection remains highly variable and inconsistent. In this work, we aim to develop an algorithm (ARDSFlag) to automate the diagnosis of ARDS based on the Berlin definition. We also aim to develop a visualization tool that helps clinicians efficiently assess ARDS criteria.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy.

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area.

Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm.

Background: The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.

Methods And Results: Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis).

A Multimodel-Based Screening Framework for C-19 Using Deep Learning-Inspired Data Fusion.

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19.

A hybrid swarm intelligence algorithm for region-based image fusion.

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added.

In-hospital outcomes and postdischarge mortality in patients with acute coronary syndrome and atrial fibrillation.

Background: It is unclear whether advances in management of acute coronary syndrome (ACS) and introduction of novel oral anticoagulants have changed outcomes in patients with ACS with concomitant atrial fibrillation (AF).

Objective: This study aimed to examine the incidence of AF in patients admitted for ACS and to evaluate its association with adverse outcomes, given the recent advances in management of both diseases.

Methods: Natural language processing search algorithms identified AF in patients admitted with ACS across 13 Northwell Health Hospitals from 2015 to 2021.

Hybrid WT-CNN-GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features.

Water quality indicators (WQIs), such as chlorophyll-a (Chl-a) and dissolved oxygen (DO), are crucial for understanding and assessing the health of aquatic ecosystems. Precise prediction of these indicators is fundamental for the efficient administration of rivers, lakes, and reservoirs. This research utilized two unique DL algorithms-namely, convolutional neural network (CNNs) and gated recurrent units (GRUs)-alongside their amalgamation, CNN-GRU, to precisely gauge the concentration of these indicators within a reservoir.

AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML).

Should Transport Ventilators Be Used in Times of Crisis? The Use of Emergency Authorized Nonconventional Ventilators Is Associated With Mortality Among Patients With COVID-19 Acute Respiratory Distress Syndrome.

Objectives: Nonconventional ventilators (NCVs), defined here as transport ventilators and certain noninvasive positive pressure devices, were used extensively as crisis-time ventilators for intubated patients with COVID-19. We assessed whether there was an association between the use of NCV and higher mortality, independent of other factors.

Design: This is a multicenter retrospective observational study.

Levenberg-Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning.

Watermarking is one of the crucial techniques in the domain of information security, preventing the exploitation of 3D Mesh models in the era of Internet. In 3D Mesh watermark embedding, moderately perturbing the vertices is commonly required to retain them in certain pre-arranged relationship with their neighboring vertices. This paper proposes a novel watermarking authentication method, called Nearest Centroid Discrete Gaussian and Levenberg-Marquardt (NCDG-LV), for distortion detection and recovery using salient point detection.

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA) based on 15 easily accessible hematological indices. A database of 1596 patients with COVID-19 was used; it was divided into 1257 training datasets (80 % of the database) for training the algorithms and 339 testing datasets (20 % of the database) to check the reliability of the algorithms.

A novel multi-hybrid differential evolution algorithm for optimization of frame structures.

Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm.

Efficient implicit constraint handling approaches for constrained optimization problems.

Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput.

Potholes and traffic signs detection by classifier with vision transformers.

Detecting potholes and traffic signs is crucial for driver assistance systems and autonomous vehicles, emphasizing real-time and accurate recognition. In India, approximately 2500 fatalities occur annually due to accidents linked to hidden potholes and overlooked traffic signs. Existing methods often overlook water-filled and illuminated potholes, as well as those shaded by trees.

Computer vision-based plants phenotyping: A comprehensive survey.

The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery.

Parkinson's disease detection based on features refinement through L1 regularized SVM and deep neural network.

In previous studies, replicated and multiple types of speech data have been used for Parkinson's disease (PD) detection. However, two main problems in these studies are lower PD detection accuracy and inappropriate validation methodologies leading to unreliable results. This study discusses the effects of inappropriate validation methodologies used in previous studies and highlights the use of appropriate alternative validation methods that would ensure generalization.

DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.

Melanoma is a severe skin cancer that involves abnormal cell development. This study aims to provide a new feature fusion framework for melanoma classification that includes a novel 'F' Flag feature for early detection. This novel 'F' indicator efficiently distinguishes benign skin lesions from malignant ones known as melanoma.

Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs.

The most widely used method for detecting Coronavirus Disease 2019 (COVID-19) is real-time polymerase chain reaction. However, this method has several drawbacks, including high cost, lengthy turnaround time for results, and the potential for false-negative results due to limited sensitivity. To address these issues, additional technologies such as computed tomography (CT) or X-rays have been employed for diagnosing the disease.

SSH-DAuth: secret sharing based decentralized OAuth using decentralized identifier.

OAuth2.0 is a Single Sign-On approach that helps to authorize users to log into multiple applications without re-entering the credentials. Here, the OAuth service provider controls the central repository where data is stored, which may lead to third-party fraud and identity theft.