Targeting the uncertainty of predictions at patient-level using an ensemble of classifiers coupled with calibration methods, Venn-ABERS, and Conformal Predictors: a …
T Pereira, S Cardoso, M Guerreiro, SC Madeira… - Journal of biomedical …, 2020 - Elsevier
Despite being able to make accurate predictions, most existing prognostic models lack a
proper indication about the uncertainty of each prediction, that is, the risk of prediction error …
proper indication about the uncertainty of each prediction, that is, the risk of prediction error …
Calibrating predictive model estimates to support personalized medicine
Objective: Predictive models that generate individualized estimates for medically relevant
outcomes are playing increasing roles in clinical care and translational research. However …
outcomes are playing increasing roles in clinical care and translational research. However …
Uncertainty estimations methods for a deep learning model to aid in clinical decision-making--a clinician's perspective
Prediction uncertainty estimation has clinical significance as it can potentially quantify
prediction reliability. Clinicians may trust'blackbox'models more if robust reliability …
prediction reliability. Clinicians may trust'blackbox'models more if robust reliability …
The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models
Background Many measures of prediction accuracy have been developed. However, the
most popular ones in typical medical outcome prediction settings require additional …
most popular ones in typical medical outcome prediction settings require additional …
Identifying unreliable predictions in clinical risk models
The ability to identify patients who are likely to have an adverse outcome is an essential
component of good clinical care. Therefore, predictive risk stratification models play an …
component of good clinical care. Therefore, predictive risk stratification models play an …
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 …
A calibration metric for risk scores with survival data
We study methods for assessing the degree of systematic over-or under-estimation, known
as calibration, of a learned risk model in an independent validation cohort. Here, we …
as calibration, of a learned risk model in an independent validation cohort. Here, we …
A patient-driven adaptive prediction technique to improve personalized risk estimation for clinical decision support
Objective Competing tools are available online to assess the risk of developing certain
conditions of interest, such as cardiovascular disease. While predictive models have been …
conditions of interest, such as cardiovascular disease. While predictive models have been …
[HTML][HTML] Clinical prediction models: evaluation matters
Clinical prediction models, also known as “prognostic models”,“risk scores”, or “prediction
rules”, have received increasing attention in recent years (1, 2). Clinical prediction models …
rules”, have received increasing attention in recent years (1, 2). Clinical prediction models …
Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability
Background Prognostic models that are accurate could help aid medical decision making.
Large observational databases often contain temporal medical data for large and diverse …
Large observational databases often contain temporal medical data for large and diverse …