在电子健康记录中识别痴呆症的基于代码的算法:弥合理论和实践之间的差距
Shanquan Chen1, Yuqi Wang2, Christoph Mueller3,4
1International Centre for Evidence in Disability, London School of Hygiene & Tropical Medicine, London, UK.
Journal of Alzheimer's disease : JAD
|September 18, 2023
概括
基于代码的算法显示出在电子健康记录中发现痴呆症的前景,达到70%的准确性. 然而,需要改进,因为30%的痴呆病例仍未被发现.
科学领域:
- 计算医学是一种计算医学.
- 医疗信息学医学信息学
- 老年学是指老年学的学科.
背景情况:
- 电子健康记录 (EHR) 越来越多地用于医学研究.
- 基于代码的算法为在EHR数据中识别诸如痴呆症等复杂疾病提供了潜在的途径.
- 准确的痴呆症检测对于及时干预和改善患者结果至关重要.
研究的目的:
- 通过使用EHR数据,评估基于代码的算法用于痴呆症检测的有效性.
- 评估这些算法的灵敏度和积极的预测值.
- 确定当前痴呆症检测方法的局限性和改进领域.
主要方法:
- 使用预定义的基于代码的算法分析电子健康记录数据.
- 计算算法性能指标,包括灵敏度和正预测值.
- 识别算法错过的情况.
主要成果:
- 基于代码的算法在痴呆症检测中实现了大约70%的灵敏度和积极的预测值.
- 很大一部分 (约30%) 的痴呆病例没有被算法检测到.
- 在区分认知衰退因素与EHR数据中的其他条件方面发现了挑战.
结论:
- 基于代码的算法是EMS中痴呆症检测的宝贵工具,但需要进一步改进.
- 算法改进是必要的,以提高灵敏度和减少错误诊断.
- 在多样化的医疗保健系统和人群中进行进一步的研究对于扩大适用性和弥合痴呆症护理差距至关重要.
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