使用机器学习分析与性结肠炎相关的结肠直肠癌风险因素:使用日本国家数据库进行回顾性纵向研究
Miwa Hirai1, Yasuhiro Kanatani2, Takashi Ueda1
1Division of Gastroenterology and Hepatology, Department of Internal Medicine, Tokai University School of Medicine, 143 Shimokasuya, Isehara 259-1193, Japan.
性结肠炎 (UC) 患者的结肠直肠癌 (CRC) 风险增加. 伪多和发育不良是关键的危险因素,而5-ASA可能会降低风险. 机器学习确定了胰腺炎和直肠炎是CRC发展的重要指标.
科学领域:
- 胃肠病学 胃肠病学
- 在瘤学瘤学.
- 流行病学 流行病学
背景情况:
- 性结肠炎 (UC) 是一种慢性炎症性肠病.
- UC显著增加了结直肠癌 (CRC) 的风险.
- 了解UC相关的CRC流行病学对于早期检测和预防至关重要.
研究的目的:
- 为了澄清日本UC相关CRC的临床流行病学.
- 确定UC患者中CRC的风险因素和预后指标.
- 使用机器学习开发UC相关CRC的预测模型.
主要方法:
- 利用了日本全国难以治愈疾病的数据 (2003-2011财年).
- 使用标准化发病率计算了相对发病率 (RR).
- 雇佣了Cox的比例危险模型和用于预后分析的机器学习.
主要成果:
- 在78,556名UC患者中发现了141例CRC病例.
- 在25-39岁的年龄组中,CRC风险达到峰值.
- 风险因素包括伪多,异常的密码结构和发育不良;5-ASA显示有保护作用.
- 机器学习将胰腺炎确定为高风险因素.
结论:
- 伪多和发育不良是UC中显著的CRC风险因素.
- 机器学习强调了50多岁的胰腺炎和30多岁的直肠炎作为特定风险.
- 研究结果支持加强与UC相关的CRC的早期检测和预防策略.
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