Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a sco** review
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 …
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
Objective To review and appraise the validity and usefulness of published and preprint
reports of prediction models for prognosis of patients with covid-19, and for detecting people …
reports of prediction models for prognosis of patients with covid-19, and for detecting people …
Calculating the sample size required for develo** 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 risk of bias and applicability of prediction model studies: explanation and elaboration
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 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 …
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 …
Comparison of eight prehospital stroke scales to detect intracranial large-vessel occlusion in suspected stroke (PRESTO): a prospective observational study
Background Due to the time-sensitive effect of endovascular treatment, rapid prehospital
identification of large-vessel occlusion in individuals with suspected stroke is essential to …
identification of large-vessel occlusion in individuals with suspected stroke is essential to …
Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the …
Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the …
A calibration hierarchy for risk models was defined: from utopia to empirical data
Objective Calibrated risk models are vital for valid decision support. We define four levels of
calibration and describe implications for model development and external validation of …
calibration and describe implications for model development and external validation of …
Validation of a prediction tool for chemotherapy toxicity in older adults with cancer
Purpose Older adults are at increased risk for chemotherapy toxicity, and standard oncology
assessment measures cannot identify those at risk. A predictive model for chemotherapy …
assessment measures cannot identify those at risk. A predictive model for chemotherapy …