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 …
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models
Objectives The objective of this study was to compare performance of logistic regression
(LR) with machine learning (ML) for clinical prediction modeling in the literature. Study …
(LR) with machine learning (ML) for clinical prediction modeling in the literature. Study …
Calibration: the Achilles heel of predictive analytics
Background The assessment of calibration performance of risk prediction models based on
regression or more flexible machine learning algorithms receives little attention. Main text …
regression or more flexible machine learning algorithms receives little attention. Main text …
The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression
R van den Goorbergh, M van Smeden… - Journal of the …, 2022 - academic.oup.com
Objective Methods to correct class imbalance (imbalance between the frequency of outcome
events and nonevents) are receiving increasing interest for developing prediction models …
events and nonevents) are receiving increasing interest for developing prediction models …
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 …
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 …
Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease
The medical field has seen a rapid increase in the development of artificial intelligence (AI)-
based prediction models. With the introduction of such AI-based prediction model tools and …
based prediction models. With the introduction of such AI-based prediction model tools and …
Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology
Objective To provide an overview and critical appraisal of early warning scores for adult
hospital patients. Design Systematic review. Data sources Medline, CINAHL, PsycInfo, and …
hospital patients. Design Systematic review. Data sources Medline, CINAHL, PsycInfo, and …
[HTML][HTML] Development and reporting of prediction models: guidance for authors from editors of respiratory, sleep, and critical care journals
DE Leisman, MO Harhay, DJ Lederer… - Critical care …, 2020 - journals.lww.com
Prediction models aim to use available data to predict a health state or outcome that has not
yet been observed. Prediction is primarily relevant to clinical practice, but is also used in …
yet been observed. Prediction is primarily relevant to clinical practice, but is also used in …
TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …
Prognosis Or Diagnosis) statement was published in 2015 to provide the minimum reporting …