部署和验证CLL治疗感染模型与EHR系统相连
Rudi Agius1, Anders C Riis-Jensen2, Bettina Wimmer2
1Department of Hematology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
NPJ digital medicine
|June 5, 2024
概括
将研究算法部署到临床实践中需要解决数据问题并确保可靠的预测. 这项研究详细介绍了慢性淋巴细胞白血病 (CLL) 感染风险模型的成功整合到EHR中,并提供了部署指南.
科学领域:
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
- 健康 数据科学 数据科学
背景情况:
- 研究算法很少被验证或集成到临床实践中.
- 部署面临数据协调,缺失数据,自动化,监控和法律合规方面的挑战.
研究的目的:
- 描述在电子健康记录 (EHR) 中部署一个高维,数据驱动的决策支持模型.
- 为在临床环境中成功部署研究算法得出实际指导方针.
主要方法:
- 在基于EPIC的丹麦EHR中部署慢性淋巴细胞白血病 (CLL) 治疗感染模型 (CLL-TIM).
- 利用了在7年的患者记录上训练的84个变量预后模型.
- 使用从2018年开始诊断的1480名CLL患者的回顾性队列验证了部署.
主要成果:
- 在部署过程中观察到关键变量的意外高缺失.
- 高维度,缺少数据处理和预测信心对于可靠的预测至关重要.
- 部署过程包括符合医疗器械法规的自动化和监控.
结论:
- 成功地部署研究算法EHR需要解决数据质量,缺失和预测信心.
- 描述的部署过程和指南可以帮助其他人将研究算法集成到临床实践中.
- 这项工作为在现实世界医疗保健环境中可靠实施预后模型提供了一个框架.
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