In this study, we aimed to develop, internally and temporally validate the machine learning models to help screen YOCRC bansed on the retrospective extracted Electronic Medical Records (EMR) data.
Diagnosis of young-onset colorectal cancer (YOCRC) has become more common in recent decades. Screening CRC among younger adults still remains a challenge. In this study, We plan to retrospectively extracte the relevant clinical data of young individuals who underwent colonoscopy from 2013 to 2022 using Electronic Medical Record (EMR). Multiple supervised machine learning techniques will be applied to distinguish YOCRC and non-YOCRC individuals, the above classifiers will be trained and internally validated in the training dataset and internal validation dataset admitted between 2013 and 2021, respectively. We will also assess the temporal external validity of the classifiers based on the admissions from 2022.
Study Type
OBSERVATIONAL
Enrollment
11,000
This study used clinical data and machine learning model to screen young-onset colorectal cancer.
Renmin Hospital of Wuhan University
Wuhan, Hubei, China
The performance of machine learning screening models
The performance of young-onset colorectal cancer screening models will be assessed by calculating the area under the receiver operating characteristic (ROC) curve (AUC), Accuracy, Recall, Specificity, Negative predictive value (NPV), Positive predictive value (PPV, or called Precision).
Time frame: through study completion, an average of 1 year
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