Objectives In this systematic review we aimed at assessing how artificial intelligence (AI),
including machine learning (ML) techniques have been deployed to predict, diagnose, and …

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting
the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease …

Artificial intelligence (AI), as an advanced science technology, has been widely used in
medical fields to promote medical development, mainly applied to early detections, disease …

Machine learning algorithms are excellent techniques to develop prediction models to
enhance response and efficiency in the health sector. It is the greatest approach to avoid the …

The study aims to develop AICare, an interpretable mortality prediction model, using
electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) …

Automatic assessment of impairment and disease severity is a key challenge in data-driven
medicine. We propose a framework to address this challenge, which leverages AI models …

Artificial intelligence (AI) has been applied widely in almost every area of our daily lives, due
to the growth of computing power, advances in methods and techniques, and the explosion …

Artificial intelligence (AI) is considered as the next natural progression of traditional
statistical techniques. Advances in analytical methods and infrastructure enable AI to be …

A huge array of data in nephrology is collected through patient registries, large
epidemiological studies, electronic health records, administrative claims, clinical trial …

Risk prediction models are statistical models that estimate the probability of individuals
having a certain disease or clinical outcome based on a range of characteristics, and they …