开发和评估一种机器学习模型,以预测性结肠炎患者的意外再入院风险
Tianqi Wang1, Yujie Zhao2, Xiaobin Zhao1
1First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
一个新的机器学习模型准确地预测了性结肠炎 (UC) 患者在一年内无计划的医院再入院. 该工具有助于临床医生进行个性化风险评估,并改善这种慢性炎症性肠病的患者管理.
科学领域:
- 胃肠病学 胃肠病学
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
背景情况:
- 性结肠炎 (UC) 是一种慢性炎症性肠病,其特点是不可预测的爆发和缓解.
- 由于疾病的可变性,UC患者经常住院.
- 现有的风险评分系统在预测UC患者再入院时缺乏足够的准确性.
研究的目的:
- 开发和验证基于机器学习 (ML) 的模型,用于预测UC患者1年计划外再入院的风险.
- 通过使用ML技术,识别重新接收的关键预测因素.
- 创建一个用户友好的工具,用于临床应用.
主要方法:
- 一个回顾性队列 (n=324) 被用于开发ML模型,在一个前性队列 (n=137) 上进行外部验证.
- 输入变量包括人口统计,病史,药物,症状,实验室发现和内镜数据.
- 递归特征消除 (RFE) 选择了最佳特征,并构建了八个ML模型并进行了交叉验证. 随机森林 (RF) 模型是因为它的性能而被选择的.
主要成果:
- 该RFE算法确定了C反应蛋白,红细胞沉积率,红细胞计数,增加肠道排便频率和血小板计数作为关键预测因素.
- 所有的ML模型都表现出强大的预测能力,在训练队列中AUC>0.75.
- 射频模型表现出极好的稳定性和通用性,AUC值为0.936 (训练),0.815 (内部验证) 和0.813 (外部验证).
结论:
- 开发的基于射频的模型准确地预测了UC患者1年的计划外再入院风险.
- 基于RF模型的网络风险计算器为临床医生提供了个性化风险评估的宝贵工具.
- 这种工具可以增强治疗性结肠炎的患者管理策略.
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