The analysis and quality assessment of translation strategies in subtitling culturally specific references: .

Subtitling poses significant challenges, particularly when dealing with culturally specific/bound source texts (STs). This research paper aims to identify and assess the strategies employed by professional translators in rendering culturally specific references from Egyptian Arabic into English in the award-winning Egyptian movie, . In this study, Pedersen's (2011) typology of translation strategies was utilized to identify the strategies employed, while Pedersen's (2017) functional equivalence, acceptability, and readability (FAR) model was employed to assess the quality of the subtitles.

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically.

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.

The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials.

CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19.

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the (CPAS)-a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital.

Interleukin-17 haplotyping predicts hepatocellular carcinoma in sofosbuvir, pegylated interferon-alpha-2a & ribavirin treated chronic hepatitis C patients.

Suspect has been directed towards some direct acting antivirals (DAAs) due to their reported association with hepatocellular carcinoma (HCC) development in chronic hepatitis C (CHC) patients. The mechanisms behind HCC development, following CHC treatment, were not well understood and may be linked to genetic variabilities in different patients which affect several cytokine productions involved in angiogenesis and inflammation. Of these variabilities, is the genetic polymorphisms in the interleukin-17 (IL-17) A receptor gene.

From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges.

Clinical decision making needs to be supported by evidence that treatments are beneficial to individual patients. Although randomized control trials (RCTs) are the gold standard for testing and introducing new drugs, due to the focus on specific questions with respect to establishing efficacy and safety vs. standard treatment, they do not provide a full characterization of the heterogeneity in the final intended treatment population.

Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants.

Background: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them.

Prognostication and Risk Factors for Cystic Fibrosis via Automated Machine Learning.

Accurate prediction of survival for cystic fibrosis (CF) patients is instrumental in establishing the optimal timing for referring patients with terminal respiratory failure for lung transplantation (LT). Current practice considers referring patients for LT evaluation once the forced expiratory volume (FEV) drops below 30% of its predicted nominal value. While FEV is indeed a strong predictor of CF-related mortality, we hypothesized that the survival behavior of CF patients exhibits a lot more heterogeneity.

Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation.

Background: Risk prediction is crucial in many areas of medical practice, such as cardiac transplantation, but existing clinical risk-scoring methods have suboptimal performance. We develop a novel risk prediction algorithm and test its performance on the database of all patients who were registered for cardiac transplantation in the United States during 1985-2015.

Methods And Findings: We develop a new, interpretable, methodology (ToPs: Trees of Predictors) built on the principle that specific predictive (survival) models should be used for specific clusters within the patient population.

Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes.

Objective: In this paper, we develop a personalized real-time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs; the proposed risk scoring system ensures timely intensive care unit admissions for clinically deteriorating patients.

Methods: The risk scoring system is based on the idea of sequential hypothesis testing under an uncertain time horizon. The system learns a set of latent patient subtypes from the offline electronic health record data, and trains a mixture of Gaussian Process experts, where each expert models the physiological data streams associated with a specific patient subtype.