Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …

Recent years have seen the rapid proliferation of clinical prediction models aiming to
support risk stratification and individualized care within psychiatry. Despite growing interest …

Objectives Missing data is a common problem during the development, evaluation, and
implementation of prediction models. Although machine learning (ML) methods are often …

Abstract Background and Objectives We sought to summarize the study design, modelling
strategies, and performance measures reported in studies on clinical prediction models …

Objectives We evaluated the presence and frequency of spin practices and poor reporting
standards in studies that developed and/or validated clinical prediction models using …

Background While many studies have consistently found incomplete reporting of regression-
based prediction model studies, evidence is lacking for machine learning-based prediction …

Background Depression is one of the most significant health conditions in personal, social,
and economic impact. The aim of this review is to summarize existing literature in which …

Artificial intelligence (AI) is critical to harnessing value from exponentially growing health
and healthcare data. Expectations are high for AI solutions to effectively address current …

Aims Heart failure (HF) is one of the most frequent diagnoses for 30-day readmission after
hospital discharge. Nurses have a role in reducing unplanned readmission and providing …

Background The applications of artificial intelligence (AI) processes have grown significantly
in all medical disciplines during the last decades. Two main types of AI have been applied in …