一个用于临床决策的预测机器学习模型,用于性结肠炎洗微生物移植的临床决策
Sheng Zhang1,2, Gaochen Lu1,2, Weihong Wang1,2
1Department of Microbiota Medicine & Medical Center for Digestive Diseases, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
这项研究开发了一种机器学习模型,用于预测性结肠炎 (UC) 患者洗净微生物群移植 (WMT) 的有效性,以帮助个性化胃肠道疾病治疗的临床决策.
科学领域:
- 胃肠病学和肝病学
- 计算生物学和生物信息学
- 翻译医学是一种翻译医学.
背景情况:
- 性结肠炎 (UC) 管理寻求改善临床决策工具.
- 机器学习 (ML) 在分析临床数据以优化治疗方面表现有前途.
- 洗微生物群移植 (WMT) 是一种新兴的UC治疗方法.
研究的目的:
- 开发和验证一种机器学习模型,用于预测UC患者对WMT的一个月的临床反应.
- 为临床医生和患者提供一种新的评估系统,以优化WMT治疗策略.
- 确定与UC中WMT疗效相关的关键临床指标.
主要方法:
- 一组366名接受WMT的UC患者被追溯分析.
- 使用临床指标构建机器学习模型组合,以预测治疗反应.
- 内部和外部验证数据集被用于评估模型性能.
主要成果:
- 对WMT反应的关键预测因素包括间接胆红素,激活的抗素III,排便频率,胆酶,年龄,肌酸激酶,HCO3-和血时间.
- 该模型在内部验证中实现了0.769 ± 0.019的AUC,在外部验证中达到0.614 ± 0.017.
- 基于该模型开发了一个可访问的在线工具 (https://wmtpredict.streamlit.app).
结论:
- 本研究介绍了第一个ML模型来预测UC的一个月WMT响应.
- 这些发现突显了ML在推进针对胃肠道疾病的个性化WMT策略方面的潜力.
- 开发的模型为优化UC治疗决策提供了有价值的工具.
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