Artificial intelligence in cardiology
Artificial intelligence and machine learning are poised to influence nearly every aspect of the
human condition, and cardiology is not an exception to this trend. This paper provides a …
human condition, and cardiology is not an exception to this trend. This paper provides a …
Towards better clinical prediction models: seven steps for development and an ABCD for validation
Clinical prediction models provide risk estimates for the presence of disease (diagnosis) or
an event in the future course of disease (prognosis) for individual patients. Although …
an event in the future course of disease (prognosis) for individual patients. Although …
A two-step workflow based on plasma p-tau217 to screen for amyloid β positivity with further confirmatory testing only in uncertain cases
Cost-effective strategies for identifying amyloid-β (Aβ) positivity in patients with cognitive
impairment are urgently needed with recent approvals of anti-Aβ immunotherapies for …
impairment are urgently needed with recent approvals of anti-Aβ immunotherapies for …
[HTML][HTML] Variable selection strategies and its importance in clinical prediction modelling
Clinical prediction models are used frequently in clinical practice to identify patients who are
at risk of develo** an adverse outcome so that preventive measures can be initiated. A …
at risk of develo** an adverse outcome so that preventive measures can be initiated. A …
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 …
Sample size for binary logistic prediction models: beyond events per variable criteria
Binary logistic regression is one of the most frequently applied statistical approaches for
develo** clinical prediction models. Developers of such models often rely on an Events …
develo** clinical prediction models. Developers of such models often rely on an Events …
Salivary MicroRNA signature for diagnosis of endometriosis
Background: Endometriosis diagnosis constitutes a considerable economic burden for the
healthcare system with diagnostic tools often inconclusive with insufficient accuracy. We …
healthcare system with diagnostic tools often inconclusive with insufficient accuracy. We …
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 …
[HTML][HTML] The number of subjects per variable required in linear regression analyses
Objectives To determine the number of independent variables that can be included in a
linear regression model. Study Design and Setting We used a series of Monte Carlo …
linear regression model. Study Design and Setting We used a series of Monte Carlo …
Alternative fistula risk score for pancreatoduodenectomy (a-FRS): design and international external validation
Objective: The aim of this study was to develop an alternative fistula risk score (a-FRS) for
postoperative pancreatic fistula (POPF) after pancreatoduodenectomy, without blood loss as …
postoperative pancreatic fistula (POPF) after pancreatoduodenectomy, without blood loss as …