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 …
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 …
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on …
Introduction The Transparent Reporting of a multivariable prediction model of Individual
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …
Prognosis Or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias …
[PDF][PDF] Living guidance for clinical management of COVID-19: living guidance, 23 November 2021
World Health Organization - 2021 - apps.who.int
This guideline is based on the above strategic priorities, and is intended for clinicians
involved in the care of patients with suspected or confirmed COVID-19. It is not meant to …
involved in the care of patients with suspected or confirmed COVID-19. It is not meant to …
[PDF][PDF] COVID-19 clinical management: living guidance, 25 January 2021
( 2021 World Health Organization - 2021 - apps.who.int
The guidance in this document is based on the above strategic priorities, and is intended for
clinicians involved in the care of patients with suspected or confirmed COVID-19. It is not …
clinicians involved in the care of patients with suspected or confirmed COVID-19. It is not …
Designing deep learning studies in cancer diagnostics
A Kleppe, OJ Skrede, S De Raedt, K Liestøl… - Nature Reviews …, 2021 - nature.com
The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …
and systems are frequently claimed to perform comparable with or better than clinicians …
[PDF][PDF] Clinical management of COVID-19: living guideline, 13 January 2023
World Health Organization - 2023 - apps.who.int
Post COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-
CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at …
CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at …
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 …
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies
Objective To systematically examine the design, reporting standards, risk of bias, and claims
of studies comparing the performance of diagnostic deep learning algorithms for medical …
of studies comparing the performance of diagnostic deep learning algorithms for medical …