机器学习用于在诊断前几年从常规血液测试中预测克罗恩病:来自epi-IIRN队列的结果
Raffi Lev-Tzion1, Amir S Dolev2, Shira Yuval Bar-Asher3
1Juliet Keidan Institute of Pediatric Gastroenterology Hepatology and Nutrition, The Eisenberg R&D Authority, Shaare Zedek Medical Center, Jerusalem, Israel.
常规血液检查可以在诊断前几年预测克罗恩病 (CD). 一个机器学习模型识别了重要的血液标记物,有助于早期检测和潜在的预防策略的炎症性肠病 (IBD).
科学领域:
- 胃肠病学 胃肠病学
- 生物标志物 生物标志物
- 预测分析是一种预测分析.
背景情况:
- 炎症性肠病 (IBD),包括克罗恩病 (CD) 和性结肠炎 (UC),对健康造成重大负担.
- 早期发现IBD对于有效管理和改善患者的治疗结果至关重要.
- 在临床诊断前几年确定IBD发生的预测标记仍然是一个挑战.
研究的目的:
- 研究常规血液检查在诊断前几年预测IBD发生的有用性.
- 为了确定特定的血液检测标志物,这些标志物在后来患有CD的个体中显示出一致的差异.
- 开发和评估一种机器学习模型,以根据诊断前的血液检测结果预测未来的CD诊断.
主要方法:
- 使用了全国范围的epi-以色列炎症性肠病 (IBD) 研究核 (IIRN) 队列.
- 包括在2005-2020年间诊断的IBD患者,与两个非IBD对照匹配.
- 分析了84项血液测试,在诊断前15年内进行,使用Welch的t测试和机器学习来预测CD.
主要成果:
- 在患有CD的成年人中,在诊断前一年以上观察到29项血液测试中的显著差异;3项在10年前有所不同.
- 在患有CD的儿童中,17项血液检测显示在诊断前一年内存在一致的差异.
- 一个机器学习模型预测成人CD的AUC为0.70一年和0.61七年预诊;UC没有发现显著差异.
结论:
- 常规的血液检查在克罗恩病诊断前几年就会显示出可检测的变化.
- 一个机器学习模型可以预测未来的CD诊断,为早期查和预防提供潜力.
- 这些发现强调了开发IBD新查和预测模型的可能性.
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