Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.

Journal: BMJ (Clinical research ed.)

Published Date: Nov 4, 2020

Abstract

OBJECTIVE: To assess the consistency of machine learning and statistical techniques in predicting individual level and population level risks of cardiovascular disease and the effects of censoring on risk predictions.

Authors

Yan Li

Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.
Matthew Sperrin
Darren M Ashcroft

Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Tjeerd Pieter van Staa

Health e-Research Centre, Health Data Research UK North, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, Manchester M13 9PL, UK.

Keywords

Adult Calibration Cardiovascular Diseases Decision Making England Female Humans Longitudinal Studies Machine Learning Male Medical Record Linkage Middle Aged Models, Statistical Predictive Value of Tests Risk Assessment Survival Analysis

External Resources

View on PubMed Access via DOI PubMed (33148619)

Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.

Abstract

Authors

Keywords

External Resources

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