在2型糖尿病中预测慢性病 使用自然语言处理对医疗保健数据的预测
Juan F Navarro-González1,2,3,4, Leopoldo Pérez de Isla5, Gloria Cánovas Molina6
1Unidad de Investigación y Servicio de Nefrología, Hospital Universitario Nuestra Señora de Candelaria, Santa Cruz de Tenerife, Spain.
Kidney diseases (Basel, Switzerland)
|January 9, 2026
概括
使用非结构化电子健康记录 (EHR) 数据,可以预测2型糖尿病 (T2DM) 中的慢性病 (CKD). 这项研究为T2DM患者开发了为期2年的CKD风险模型,强调了需要更好的EHR数据质量.
科学领域:
- 医疗信息学 医疗信息学
- 腎臟病學 (nephrology) 是一種醫學專業.
- 数据科学数据科学数据科学
背景情况:
- 2型糖尿病 (T2DM) 患者经常出现严重并发症,如慢性病 (CKD).
- 电子健康记录 (EHR) 包含有价值的非结构化数据,用于预测T2DM并发症.
- 在T2DM中早期发现CKD对于有效的管理和风险分层至关重要.
研究的目的:
- 评估通过自然语言处理 (NLP) 和机器学习 (ML) 处理的非结构化EHR数据的实用性,用于开发T2DM患者中CKD的预测模型.
- 创建和验证T2DM患者中CKD发展的2年预测模型.
- 将表现最好的预测模型集成到基于网络的工具中,用于早期CKD检测和风险分层.
主要方法:
- 一项多中心的回顾性研究,利用了来自西班牙八家医院 (2013-2018) 的EHR数据.
- 数据的提取和分析采用了基于SNOMED CT术语的NLP和ML技术 (EHRead®).
- 训练并验证了后勤回归模型,以预测T2DM患者在基线没有CKD的2年CKD风险.
主要成果:
- 该研究包括316,597名T2DM患者,用于模型开发.
- 使用27个预测因素的后勤回归模型实现了0.72的AUC,而减少的8和10个预测模型显示了可比性能.
- 一个经过临床改进的8个预测器模型被选中用于在基于网络的工具中实现.
结论:
- 使用NLP和ML处理的非结构化EHR数据可以有效地开发T2DM患者2年CKD风险的预测模型.
- 开发的模型有助于早期检测和T2DM中CKD风险分层.
- 提高电子健康记录数据的完整性对于提高未来预测模型的准确性和实用性至关重要.
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