Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …

Aims We conducted a systematic review assessing the reporting quality of studies validating
models based on machine learning (ML) for clinical diagnosis, with a specific focus on the …

Background: Adherence to evidence-based practice is indispensable in healthcare.
Recently, the utility of artificial intelligence (AI)-based models in healthcare has been …

Background Diabetes mellitus (DM) is a major health concern among children with the
widespread adoption of advanced technologies. However, concerns are growing about the …

Background As more and more researchers are turning to big data for new opportunities of
biomedical discoveries, machine learning models, as the backbone of big data analysis, are …

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

Background Common data models (CDMs) are essential tools for data harmonization, which
can lead to significant improvements in the health domain. CDMs unite data from disparate …

Background: Contributing to health informatics research means using conceptual models
that are integrative and explain the research in terms of the two broad domains of health …

Introduction Studies addressing the development and/or validation of diagnostic and
prognostic prediction models are abundant in most clinical domains. Systematic reviews …

Introduction The Transparent Reporting of a multivariable prediction model of Individual
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …