Identification of colorectal cancer using structured and free text clinical data. (26th October 2022)

• Record Type: Journal Article
• Authors: Redd, Douglas F; Shao, Yijun; Zeng-Treitler, Qing; Myers, Laura J; Barker, Barry C; Nelson, Stuart J; Imperiale, Thomas F
• Is Part Of: Health informatics journal. Volume 28:Number 4(2022)
• Publication Date: 2022-10-26
• Subjects: Colon cancer -- feature utilization -- machine learning -- model comparison -- statistical models; Medical informatics -- Periodicals; 610.285
• DOI: 10.1177/14604582221134406
• Languages: English
• ISSNs: 1460-4582

Abstract

Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even with the recent screening guidelines recommending that screening begins at age 45, nearly half of all early-onset colorectal cancer will be missed. Methods are needed to identify high-risk individuals in this age group for targeted screening. Colorectal cancer studies, as with other clinical studies, have required labor intensive chart review for the identification of those affected and risk factors. Natural language processing and machine learning can be used to automate the process and enable the screening of large numbers of patients. This study developed and compared four machine learning and statistical models: logistic regression, support vector machine, random forest, and deep neural network, in their performance in classifying colorectal cancer patients. Excellent classification performance is achieved with AUCs over 97%.