Department of Gastroenterology, The Affiliated Huai'an Hospital of Xuzhou Medical University, the Second People's Hospital of Huai'an, Huaian, 223002, China.
College of Computer Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
Sci Rep. 2019 Nov 22;9(1):17418. doi: 10.1038/s41598-019-54031-2.
The incidence of colorectal cancer (colorectal cancer, CRC) in China has increased in recent years, and its mortality rate has become one of the highest among all cancers. CRC also increasingly affects people's health and quality of life, and the workloads of medical doctors have further increased due to the lack of sufficient medical resources in China. The goal of this study was to construct an automated expert system using a deep learning technique to predict the probability of early stage CRC based on the patient's case report and the patient's attributes. Compared with previous prediction methods, which are either based on sophisticated examinations or have high computational complexity, this method is shown to provide valuable information such as suggesting potentially important early signs to assist in early diagnosis, early treatment and prevention of CRC, hence helping medical doctors reduce the workloads of endoscopies and other treatments.
近年来，中国结直肠癌（colorectal cancer，CRC）的发病率不断上升，其死亡率已成为所有癌症中最高的之一。CRC 也越来越影响人们的健康和生活质量，由于中国缺乏足够的医疗资源，医生的工作量进一步增加。本研究的目的是使用深度学习技术构建一个自动化专家系统，根据患者的病例报告和患者的属性来预测早期 CRC 的概率。与以前的预测方法相比，这些方法要么基于复杂的检查，要么计算复杂度高，该方法被证明可以提供有价值的信息，例如提示潜在的重要早期迹象，以协助早期诊断、早期治疗和预防 CRC，从而帮助医生减少内窥镜检查和其他治疗的工作量。
