This chapter goes through the steps required to train and validate a simple, machine learning-based clinical prediction model for any continuous outcome. We supply fully structured code for the readers to download and execute in parallel to this sect...
We illustrate the steps required to train and validate a simple, machine learning-based clinical prediction model for any binary outcome, such as, for example, the occurrence of a complication, in the statistical programming language R. To illustrate...
BACKGROUND: Multivariate prognostic analysis has been traditionally performed by regression models. However, many algorithms capable of translating an infinity of patterns into probabilities have emerged. The comparative accuracy of artificial intell...
There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is d...
The superior performance of machine-learning scoring functions for docking has caused a series of debates on whether it is due to learning knowledge from training data that are similar in some sense to the test data. With a systematically revised met...
Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are...
BACKGROUND: The prevalence of type 2 diabetes has increased substantially in India over the past 3 decades. Undiagnosed diabetes presents a public health challenge, especially in rural areas, where access to laboratory testing for diagnosis may not b...
The accompanying article by Jiang et al. (Am J Epidemiol. 2021;190(9):1830-1840) extends quantitative bias analysis from the realm of statistical models to the realm of machine learning algorithms. Given the rooting of statistical models in the spiri...
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