Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data.

Journal: Digestive diseases and sciences

Published Date: Oct 1, 2017

Abstract

BACKGROUND: Machine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral.

Authors

Mark C Hornbrook

Kaiser Permanente Center for Health Research, 3800 North Interstate Avenue, Portland, OR, 97227-1110, USA. mark.c.hornbrook@gmail.com.
Ran Goshen

Medial EarlySign, Kfar Malal, Israel.
Eran Choman

Medial EarlySign, Kfar Malal, Israel.
Maureen O'Keeffe-Rosetti

Kaiser Permanente Center for Health Research, 3800 North Interstate Avenue, Portland, OR, 97227-1110, USA.
Yaron Kinar

Medial Research, Kfar Malal, Israel.
Elizabeth G Liles

Kaiser Permanente Center for Health Research, 3800 North Interstate Avenue, Portland, OR, 97227-1110, USA.
Kristal C Rust

Kaiser Permanente Center for Health Research, 3800 North Interstate Avenue, Portland, OR, 97227-1110, USA.

Keywords

Adult Age Factors Aged Aged, 80 and over Algorithms Area Under Curve Blood Cell Count Colonoscopy Colorectal Neoplasms Data Mining Diagnosis, Computer-Assisted Early Detection of Cancer Female Humans Machine Learning Male Middle Aged Odds Ratio Predictive Value of Tests Referral and Consultation Registries Reproducibility of Results Risk Factors ROC Curve Sex Factors

External Resources

View on PubMed Access via DOI PubMed (28836087)

Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data.

Abstract

Authors

Keywords

External Resources

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