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

Abstract The Internet of Things (IoT) has transformed many aspects of modern life, from
healthcare and transportation to home automation and industrial control systems. However …

Exploiting existing longitudinal data cohorts can bring enormous benefits to the medical
field, as many diseases have a complex and multi-factorial time-course, and start to develop …

Importance Artificial intelligence (AI) has gained considerable attention in health care, yet
concerns have been raised around appropriate methods and fairness. Current AI reporting …

Importance Finding effective and scalable solutions to address diagnostic delays and
disparities in autism is a public health imperative. Approaches that integrate eye-tracking …

Cumulative studies have shown that many long non-coding RNAs (lncRNAs) are crucial in a
number of diseases. Predicting potential lncRNA-disease associations (LDAs) can facilitate …

This review aimed to specify different concepts that are essential to the development of
medical devices (MDs) with artificial intelligence (AI)(AI-based MDs) and shed light on how …

Background: With the increasing availability of data, computing resources, and easier-to-use
software libraries, machine learning (ML) is increasingly used in disease detection and …

Motivation Although machine learning models are commonly used in medical research,
many analyses implement a simple partition into training data and hold-out test data, with …

Objective Class imbalance is sometimes considered a problem when develo** clinical
prediction models and assessing their performance. To address it, correction strategies …