Colorectal cancer (CRC) is the third most common cancer in terms of the number of cases and deaths in men and women in the USA. According to the Centers for Disease Control and Prevention, the CRC screening compliance rate remains low in the United States. It is even more concerning that the number of cases and deaths due to CRC is increasing in the younger population, for which there is no guideline to get colonoscopy screening. In this paper, we develop a novel network-based model to identify patients under 50 years of age having a high risk of CRC, particularly those who do not have a family history. Our model can help predict which patients are at risk of developing colorectal cancer to aid the practicing primary care physician and gastroenterologist to triage patients into meaningful risk groups to provide accelerated diagnostic steps and care to these patients. The model uses our proposed variables created through comorbidity network matrices obtained from CRC and non-CRC patients as inputs. We used the electronic medical records of thousands of CRC and non-CRC patients to develop and validate the models. Our model for younger patients correctly predicted 73.2% of the patients with future diagnoses with an area under the ROC curve of 0.81. At the 50% sensitivity, the false positive rate was 11.5%. The performance of our model is the highest in the current state-of-the-art. Our proposed variables quantifying the interactions between multiple diseases can also be adapted in future predictions of other diseases in a patient.

就美国男性和女性的病例数和死亡人数而言，结直肠癌 (CRC) 是第三大最常见的癌症。根据疾病控制中心和预防方面，美国的 CRC 筛查依从率仍然很低。更令人担忧的是，年轻人群中因 CRC 导致的病例数和死亡人数正在增加，而没有针对这些人群进行结肠镜检查的指南。在本文中，我们开发了一种新的基于网络的模型来识别 50 岁以下具有高 CRC 风险的患者，尤其是那些没有家族史的患者。我们的模型可以帮助预测哪些患者有患结直肠癌的风险，以帮助执业初级保健医生和胃肠病学家将患者分类为有意义的风险组，从而为这些患者提供加速诊断步骤和护理。该模型使用我们通过合并症网络矩阵创建的建议变量从 CRC 和非 CRC 患者获得作为输入。我们使用了数千名 CRC 和非 CRC 患者的电子病历来开发和验证模型。我们针对年轻患者的模型正确预测了 73.2% 的未来诊断患者，ROC 曲线下面积为 0.81。在 50% 的灵敏度下，假阳性率为 11.5%。我们模型的性能是目前最先进的。我们提出的量化多种疾病之间相互作用的变量也可以用于未来对患者其他疾病的预测。