Beyond accuracy: Measures for assessing machine learning models, pitfalls and guidelines
Pattern recognition predictive models have become an important tool for analysis of
neuroimaging data and answering important questions from clinical and cognitive …
neuroimaging data and answering important questions from clinical and cognitive …
Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating
individual differences in various behavioral phenotypes and clinical outcomes is of growing …
individual differences in various behavioral phenotypes and clinical outcomes is of growing …
[HTML][HTML] Prediction of individual differences from neuroimaging data
Brain imaging has been extensively applied to many different areas of health and disease,
with many remarkable successes that, collectively, have profoundly shaped our …
with many remarkable successes that, collectively, have profoundly shaped our …
[HTML][HTML] Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models
Abstract Background and Objectives We sought to summarize the study design, modelling
strategies, and performance measures reported in studies on clinical prediction models …
strategies, and performance measures reported in studies on clinical prediction models …
The importance of being external. methodological insights for the external validation of machine learning models in medicine
Abstract Background and Objective Medical machine learning (ML) models tend to perform
better on data from the same cohort than on new data, often due to overfitting, or co-variate …
better on data from the same cohort than on new data, often due to overfitting, or co-variate …
[HTML][HTML] Targeted validation: validating clinical prediction models in their intended population and setting
Clinical prediction models must be appropriately validated before they can be used. While
validation studies are sometimes carefully designed to match an intended population/setting …
validation studies are sometimes carefully designed to match an intended population/setting …
Foundations of machine learning-based clinical prediction modeling: Part II—Generalization and overfitting
JM Kernbach, VE Staartjes - Machine Learning in Clinical Neuroscience …, 2022 - Springer
We review the concept of overfitting, which is a well-known concern within the machine
learning community, but less established in the clinical community. Overfitted models may …
learning community, but less established in the clinical community. Overfitted models may …
[HTML][HTML] Evidence of questionable research practices in clinical prediction models
Background Clinical prediction models are widely used in health and medical research. The
area under the receiver operating characteristic curve (AUC) is a frequently used estimate to …
area under the receiver operating characteristic curve (AUC) is a frequently used estimate to …
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Objective To assess the methodological quality of studies on prediction models developed
using machine learning techniques across all medical specialties. Design Systematic …
using machine learning techniques across all medical specialties. Design Systematic …
[HTML][HTML] Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
Background While many studies have consistently found incomplete reporting of regression-
based prediction model studies, evidence is lacking for machine learning-based prediction …
based prediction model studies, evidence is lacking for machine learning-based prediction …