在功能性肠道疾病中,使用机器学习对患者生活经验的深度表型化
James K Ruffle1, Michelle Henderson2, Cho Ee Ng3
1Queen Square Institute of Neurology, University College London, London, UK. j.ruffle@ucl.ac.uk.
Scientific reports
|October 9, 2025
概括
功能性肠道疾病 (FBDs) 是复杂的. 机器学习揭示了患者的生活影响,心理健康和就业是健康的更好预测因素,而不是诊断或症状严重程度.
科学领域:
- 胃肠道学和计算生物学
- 利用先进的机器学习和贝叶斯生成图框架来处理复杂的生物系统.
背景情况:
- 功能性肠道疾病 (FBDs) 呈现出显著的异质性,缺乏明确的诊断标记或普遍有效的治疗方法.
- 目前的临床管理往往依赖于诊断标签,这些标签不能完全捕捉个体患者的经验.
- 了解影响FBD患者体验的因素的复杂相互作用需要复杂的分析方法.
研究的目的:
- 开发和应用机器学习和贝叶斯生成图框架,以阐明功能性肠道疾病患者的复杂生活经验.
- 为了确定患者报告的健康,生活质量和治疗反应的关键预测因素,在一个大型的FBD队列中.
- 通过探索更广泛的患者特征,挑战FBD管理中的传统以诊断为中心的方法.
主要方法:
- 使用机器学习模型来评估59个临床因素对患者结果的预测能力.
- 利用贝叶斯的随机块模型来绘制FBD患者异质性的网络社区结构.
- 分析了大量的队列 (n=1175),包括人口统计,诊断,症状,生活影响,心理健康,医疗保健准入和治疗有效性.
主要成果:
- 机器模型确定了生活影响,心理健康,就业状况和年龄作为患者报告的健康和生活质量的主要预测因素,表现优于诊断组或症状严重程度.
- 预测准确度包括:个人健康评级 (R2 0.35),焦虑/抑郁症严重程度 (R2 0.54),就业状况 (平衡准确率为96%),医疗出勤率 (R2 0.71) 和治疗有效率 (R2 0.08-0.41).
- 观察到治疗反应的分层,其中对一种治疗有反应的患者更有可能对其他治疗有反应,这表明了不同的患者子组.
结论:
- 对FBD的临床评估应优先考虑整体观点,重点关注更广泛的生活影响,心理健康和就业状况,而不是严格的诊断分类.
- 鉴定到的预测因素对完善临床实践和设计更有效的FBD临床试验具有重大意义.
- 需要进行进一步的研究,以探索功能性肠道疾病中治疗反应和耐药性的分层.
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