基于机器学习的预测营养不良患者的死亡率,这些患者因炎症性肠病住院治疗
Berkeley N Limketkai1,2, Zhaoping Li2, Gerard E Mullin3
1Division of Digestive Diseases, Center for Inflammatory Bowel Diseases, Vatche & Tamar Manoukian, UCLA School of Medicine.
机器学习模型准确地预测了患有蛋白质热量营养不良 (PCM) 的住院炎性肠病 (IBD) 患者的死亡率. 这种工具可以改善风险分层,并可能通过早期干预来降低死亡率.
科学领域:
- 胃肠病学 胃肠病学
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
背景情况:
- 住院的炎症性肠病 (IBD) 患有营养不良的患者面临高发病率和死亡率风险.
- 有效的风险分层对于及时的医疗和营养干预至关重要.
研究的目的:
- 开发和验证机器学习模型,以预测患有蛋白质热量营养不良 (PCM) 的住院IBD患者的死亡率.
主要方法:
- 利用了2016-2019年国家住院样本 (NIS) 数据,用于患有IBD和PCM的住院成年人.
- 使用训练和测试数据集构建和测试随机森林分类器 (RFC) 和极端梯度增强 (XGB) 模型.
- 使用2019年数据进行外部验证的模型,考虑复杂的NIS采样设计.
主要成果:
- 在879,730名患者中,1930人 (0.2%) 死亡. 已故的患者是老年人,白人,有性结肠炎,并发症和周末入院.
- 无论是RFC还是XGB模型,都表现出高预测性能,准确度,精度,灵敏度和特异性为0.99.
- 接收器运行特征曲线 (AUC) 下的面积在两种模型中均为0.91.
结论:
- 机器学习模型使用现有的临床数据准确预测营养不良IBD患者的死亡率.
- 将这些模型集成到临床实践中可以提高PCM的IBD患者的风险分层.
- 通过准确的风险预测引起的早期干预可能会降低这种高风险人群的死亡率.
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