Jose Luis

Related: Hispanics are now the largest demographic group in Texas, census data confirms

Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from being an achievable reality. Therefore, efforts have focused on predicting and explaining the risk of accidents using real-time telematics data. This study aims to analyze the factors, machine learning algorithms, and explainability methods most used to assess the risk of vehicle accidents based on driving behavior. A systematic review of the literature produced between 2013 and July 2023 on factors, prediction algorithms, and explainability methods to predict the risk of traffic accidents was carried out. Factors were categorized into five domains, and the most commonly used predictive algorithms and explainability methods were determined. We selected 80 articles from journals indexed in the Web of Science and Scopus databases, identifying 115 factors within the domains of environment, traffic, vehicle, driver, and management, with speed and acceleration being the most extensively examined. Regarding machine learning advancements in accident risk prediction, we identified 22 base algorithms, with convolutional neural network and gradient boosting being the most commonly used. For explainability, we discovered six methods, with random forest being the predominant choice, particularly for feature importance analysis. This study categorizes the factors affecting road accident risk, presents key prediction algorithms, and outlines methods to explain the risk assessment based on driving behavior, taking vehicle weight into consideration.

DALLAS — Hispanics cemented their position as the largest demographic group in Texas with over 12.1 million residents, while the state also had the largest nationwide gains in Asian and Black residents, according to new population estimates from the Census Bureau.

Department of Paralympic Sport, Wrocław University of Health and Sport Sciences, 51-612 Wrocław, Poland

Cardiology Department, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain

Multidisciplinary Breast Cancer Unit, Department of Medical Oncology, Institut Català d'Oncologia, 08908 Barcelona, Spain

Departamento de Cardiología, Fundación Valle del Lili, Cali 760032, Colombia Facultad de Ciencias de Salud, Universidad Icesi, Cali 760031, Colombia

Abstract Salivary gland squamous cell carcinomas (SG-SCCs) constitute a rare type of head and neck cancer which is linked to poor prognosis.

Although pulmonary embolism (PE) is a frequent complication in COVID-19, its consequences remain unknown. We performed pulmonary function tests, echocardiography and computed tomography pulmonary angiography and identified blood biomarkers in a cohort of consecutive hospitalized COVID-19 patients with pneumonia to describe and compare medium-term outcomes according to the presence of PE, as well as to explore their potential predictors. A total of 141 patients (56 with PE) were followed up during a median of 6 months. Post-COVID-19 radiological lung abnormalities (PCRLA) and impaired diffusing capacity for carbon monoxide (DLCOc) were found in 55.2% and 67.6% cases, respectively. A total of 7.3% had PE, and 6.7% presented an intermediate–high probability of pulmonary hypertension. No significant difference was found between PE and non-PE patients. Univariate analysis showed that age > 65, some clinical severity factors, surfactant protein-D, baseline C-reactive protein, and both peak red cell distribution width and Interleukin (IL)-10 were associated with DLCOc < 80%. A score for PCRLA prediction including age > 65, minimum lymphocyte count, and IL-1β concentration on admission was constructed with excellent overall performance. In conclusion, reduced DLCOc and PCRLA were common in COVID-19 patients after hospital discharge, but PE did not increase the risk. A PCRLA predictive score was developed, which needs further validation.

Rural Research Center (CIR), ESPOL Polytechnic University, Campus Gustavo Galindo Km 30.5 vía Perimetral, Guayaquil 090902, Ecuador