开发一种基于规则的自动算法,用于从电子健康记录中检测卵巢癌复发
Sanghee Lee1,2, Ji Hyun Kim3, Hyeong In Ha4
1Department of Cancer Control & Population Health, National Cancer Center Graduate School of Cancer Science and Policy, Goyang, Republic of Korea.
JCO clinical cancer informatics
|March 5, 2024
概括
一个自动化算法有效地从电子健康记录中检测卵巢癌复发,大大减少了手动审查时间和资源. 这种方法与传统的复查准确度非常接近,用于无复发生存率估计.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 从电子健康记录 (EHR) 中检测癌症复发通常需要广泛的手动图表审查.
- 这种手动过程耗时且资源密集,阻碍了大规模分析.
研究的目的:
- 开发一种基于规则的自动化算法,使用EHR数据检测卵巢癌 (OC) 复发.
- 评估算法的性能与手动图表审查方法相比.
主要方法:
- 开发了一种基于规则的自动复发检测算法 (Auto-Recur),使用图像,生物标志物 (CA125) 和来自EHR的治疗数据.
- 评估了Auto-Recur的灵敏度,特异性和检测复发时间的准确性.
- 将估计的无复发生存概率与回顾性图表审查结果进行了比较.
主要成果:
- 自动重复算法显著减少了手动审查时间,每10万名患者大约节省了1340天.
- 结合图像,生物标志物和治疗数据的混合算法实现了高效率 (灵敏度:93.4%,特异性:97.4%) 与最小时间误差 (8.5天).
- 估计的3年无复发生存概率 (44%) 与回顾性评估估计 (45%) 非常一致.
结论:
- 开发的基于规则的算法准确地从大规模的EHR数据中识别卵巢癌复发.
- 这种自动化方法促进了高效的EHR分析,并增强了临床研究的机会.
- 这些发现支持在临床实践中使用自动化方法来检测癌症复发.
相关概念视频
01:21Cancer Survival Analysis
345
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
345
01:15Receiver Operating Characteristic Plot
167
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
167