The use of machine learning for the identification of peripheral artery disease and future mortality risk.

Journal: Journal of vascular surgery

Published Date: Nov 1, 2016

Abstract

OBJECTIVE: A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses.

Authors

Elsie Gyang Ross

Division of Vascular Surgery, Stanford Health Care, Stanford, Calif.
Nigam H Shah

Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
Ronald L Dalman

Division of Vascular Surgery, Stanford Health Care, Stanford, Calif.
Kevin T Nead

Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pa.
John P Cooke

Department of Cardiovascular Sciences, Houston Methodist Research Institute, Houston, Tex; Center for Cardiovascular Regeneration, Houston Methodist DeBakey Heart and Vascular Center, Houston, Tex.
Nicholas J Leeper

Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, California, USA.

Keywords

Aged Algorithms Ankle Brachial Index Area Under Curve Coronary Angiography Data Mining Databases, Factual Decision Support Techniques Female Genomics Humans Linear Models Logistic Models Machine Learning Male Middle Aged Peripheral Arterial Disease Predictive Value of Tests Prognosis Reproducibility of Results Risk Assessment Risk Factors ROC Curve

External Resources

View on PubMed Access via DOI PubMed (27266594)

The use of machine learning for the identification of peripheral artery disease and future mortality risk.

Abstract

Authors

Keywords

External Resources

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