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荷兰炎症性肠病的流行,发病率和死亡率:机器学习模型的开发和外部验证
Reinier C A van Linschoten1,2,3, Nikki van Leeuwen3, David van Klaveren3
1Department of Gastroenterology & Hepatology, Franciscus Gasthuis & Vlietland, P.O. Box 10900, 3004 BA, Rotterdam, The Netherlands.
机器学习在荷兰的行政数据中准确地识别了炎症性肠病 (IBD). 肠道炎症的患病率上升速度比预期的要慢,这表明可能会向流行病学平衡转移.
科学领域:
- 流行病学 流行病学
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 大型登记册对于研究炎症性肠病 (IBD) 流行病学至关重要.
- 行政数据为大规模IBD研究提供了一个有前途的途径.
- 在行政数据中准确识别IBD病例对于可靠的流行病学研究至关重要.
研究的目的:
- 开发和验证机器学习模型,用于在行政数据中识别IBD病例.
- 确定荷兰IBD的流行率,发病率和死亡率.
- 使用机器学习模型对IBD亚型和发病年份进行分类.
主要方法:
- 使用基于人口和医院的队列开发机器学习模型 (随机森林,梯度增强树木).
- 在独立数据集上对模型的外部验证.
- 使用Brier分数,AUC,校准和准确度指标评估模型性能.
主要成果:
- 随机森林模型实现了IBD病例识别的高精度 (AUC:0.97).
- 梯度增强树模型显示IBD亚型分类的高精度 (0.95).
- 在2020年,荷兰的IBD患病率为每10万人中577.6;发病率为每10万人中20.1.
- 与一般人群相比,IBD患者的死亡率随着时间的推移而增加.
结论:
- 机器学习模型可以在行政数据中准确识别IBD病例.
- IBD患病率的增长速度慢于预期,这表明有朝着患病平衡的趋势.
- 这些发现支持使用行政数据和机器学习进行强大的IBD流行病学研究.
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