在碎片化的临床数据库中通过机器学习识别患有炎症性肠病的队列
Matthew Stammers1,2,3,4, Stephanie Sartain5,6, J R Fraser Cummings5,6
1University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK. m.stammers@soton.ac.uk.
Digestive diseases and sciences
|August 13, 2025
概括
使用计费代码识别炎症性肠病 (IBD) 患者错过了许多人. 结合11个数据库的新多模式方法确定了13,048名IBD患者,揭示了当前的方法错过了超过三分之一的队列.
科学领域:
- 胃肠病学和肝病学
- 医疗信息学 医疗信息学
- 临床流行病学临床流行病学
背景情况:
- 准确的炎症性肠病 (IBD) 队列鉴定对于患者护理和研究至关重要.
- 基于计费代码 (例如,ICD-10) 的传统方法往往由于数据库碎片化和数据缺失而不完整.
- 专业中心,就像肠道衰竭单位一样,管理复杂的IBD病例,这些病例可能会被标准编码错过.
研究的目的:
- 开发和验证新的队列检索方法,以确定一个大型大学教学医院内的炎症性肠病 (IBD) 患者总数.
- 为了比较多个临床数据库在捕获IBD患者的有效性.
- 通过当前的标准方法量化IBD队列的低估.
主要方法:
- 从2007年到2023年使用了11个不同的临床数据库 (包括计费代码,注册表,患者门户,处方和临床笔记).
- 采用处罚后勤回归 (LR) 分类器,在数据库中统计比较和验证其性能.
- 来自LR模型的综合预测与IBD诊所信件的手动审查,用于最终的队列估计.
主要成果:
- 2800名患者的验证队列证实了个别数据库的局限性.
- 受到惩罚的LR模型确定了8,159名IBD患者,AUROC为0.85.
- 将LR模型与诊所信件相结合,共估计有13,048名IBD患者,明显超过仅由ICD-10代码和药物数据识别的8,048名患者 (38.3%的低估).
结论:
- 诊断计费代码和药物数据不足以在二级护理中全面识别IBD队列.
- 多式联络,跨数据库的方法显著改善了IBD患者的捕获.
- 未来的改进需要先进的方法,如自然语言处理 (NLP),以实现更强大的患者识别.
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