Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 11,000
- 试验地点
- 1
- 主要终点
- The performance of machine learning screening models
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 49 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Newly diagnosed with CRC (YOCRC group)
- •Age at 18-49 when diagnosis (YOCRC group)
- •Never received any CRC-related treatment (YOCRC group)
- •No CRC confirmed by colonoscopy or pathology (non-YOCRC group)
- •Age at 18-49 (non-YOCRC group)
排除标准
- •Hospital stay less than 24 hours or with incomplete Complete Blood Count
- •Patients with inflammatory bowel disease or hereditary CRC syndromes
- •History of other types of primary malignant tumor and other reasons that made them unsuitable for enrollment
结局指标
主要结局
The performance of machine learning screening models
时间窗: through study completion, an average of 1 year
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).
次要结局
未报告次要终点