Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review
AAH de Hond, AM Leeuwenberg, L Hooft… - NPJ digital …, 2022 - nature.com
While the opportunities of ML and AI in healthcare are promising, the growth of complex data-
driven prediction models requires careful quality and applicability assessment before they …
driven prediction models requires careful quality and applicability assessment before they …
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 …
Metabolomic profiles predict individual multidisease outcomes
Risk stratification is critical for the early identification of high-risk individuals and disease
prevention. Here we explored the potential of nuclear magnetic resonance (NMR) …
prevention. Here we explored the potential of nuclear magnetic resonance (NMR) …
Prevalence and risk factors for mental health problems in university undergraduate students: A systematic review with meta-analysis
Background: Effective targeting of services requires that we establish which undergraduates
are at increased risk of mental health problems at university. We aimed to conduct a …
are at increased risk of mental health problems at university. We aimed to conduct a …
Calculating the sample size required for developing a clinical prediction model
Clinical prediction models aim to predict outcomes in individuals, to inform diagnosis or
prognosis in healthcare. Hundreds of prediction models are published in the medical …
prognosis in healthcare. Hundreds of prediction models are published in the medical …
PROBAST: a tool to assess the risk of bias and applicability of prediction model studies
RF Wolff, KGM Moons, RD Riley, PF Whiting… - Annals of internal …, 2019 - acpjournals.org
Clinical prediction models combine multiple predictors to estimate risk for the presence of a
particular condition (diagnostic models) or the occurrence of a certain event in the future …
particular condition (diagnostic models) or the occurrence of a certain event in the future …
Minimum sample size for external validation of a clinical prediction model with a binary outcome
In prediction model research, external validation is needed to examine an existing model's
performance using data independent to that for model development. Current external …
performance using data independent to that for model development. Current external …
PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration
KGM Moons, RF Wolff, RD Riley, PF Whiting… - Annals of internal …, 2019 - acpjournals.org
Prediction models in health care use predictors to estimate for an individual the probability
that a condition or disease is already present (diagnostic model) or will occur in the future …
that a condition or disease is already present (diagnostic model) or will occur in the future …
Minimum sample size for developing a multivariable prediction model: PART II‐binary and time‐to‐event outcomes
When designing a study to develop a new prediction model with binary or time‐to‐event
outcomes, researchers should ensure their sample size is adequate in terms of the number …
outcomes, researchers should ensure their sample size is adequate in terms of the number …
Predictive accuracy of a polygenic risk score–enhanced prediction model vs a clinical risk score for coronary artery disease
Importance The incremental value of polygenic risk scores in addition to well-established
risk prediction models for coronary artery disease (CAD) is uncertain. Objective To examine …
risk prediction models for coronary artery disease (CAD) is uncertain. Objective To examine …