Harnessing the power of artificial intelligence in predicting all-cause mortality in transcatheter aortic valve replacement: a systematic review and meta-analysis.

Objectives: In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve Implantation (TAVI) has been a topic of increasing relevance in literature. This study aims to evaluate the predictive accuracy of AI algorithms in forecasting post-TAVI mortality as compared to traditional risk scores.

Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Systematic Reviews (PRISMA) standard, a systematic review was carried out.

Maternal and Infant Dietary Patterns Are Not Related to Food Allergy Risk in Singapore Children: GUSTO Cohort Study.

Background: We previously reported that delayed allergenic food introduction in infancy did not increase food allergy risk until age 4 y within our prospective cohort. However, it remains unclear whether other aspects of maternal or infant diet play roles in the development of childhood food allergy.

Objectives: We examined the relationship between maternal pregnancy and infant dietary patterns and the development of food allergies until age 8 y.

Elastic-Net Copula Granger Causality for Inference of Biological Networks.

Aim: In bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks in order to cure and prevent related diseases like Alzheimer and cancer respectively.

Inference of biological networks using Bi-directional Random Forest Granger causality.

The standard ordinary least squares based Granger causality is one of the widely used methods for detecting causal interactions between time series data. However, recent developments in technology limit the utilization of some existing implementations due to the availability of high dimensional data. In this paper, we are proposing a technique called Bi-directional Random Forest Granger causality.

Random forest Granger causality for detection of effective brain connectivity using high-dimensional data.

Studies have shown that the brain functions are not localized to isolated areas and connections but rather depend on the intricate network of connections and regions inside the brain. These networks are commonly analyzed using Granger causality (GC) that utilizes the ordinary least squares (OLS) method for its standard implementation. In the past, several approaches have shown to solve the limitations of OLS by using diverse regularization systems.