RegEMR:一种自然语言处理系统,自动从中国电子医疗记录中识别过早的卵巢衰退
Jie Cai1, Shenglin Chen1, Siyun Guo1
1Center for Reproductive Medicine, Department of Gynecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
BMC medical informatics and decision making
|July 18, 2023
概括
一个人工智能工具,RegEMR,使用中国电子医疗记录自动化过早的卵巢衰退查. 它有效地识别风险和诊断疾病,帮助生育中心在临床决策中.
科学领域:
- 生殖医学 生殖医学
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 过早的卵巢衰退需要早期检测,以防止不育.
- 卵巢储备数据通常在电子医疗记录 (EMR) 中是无结构的.
- 手动抽象数据是耗时的,需要领域专家.
研究的目的:
- 开发一种人工智能工具 (RegEMR),用于早产卵巢衰退的自动查.
- 评估机器生成正则表达式 (RE) 与手动生成的正则表达式 (RE) 的有效性.
- 使用提取的EMR数据创建疾病评分模型,用于风险分层.
主要方法:
- RegEMR将基于规则的NLP提取器与基于知识的疾病评分模型相结合.
- 文本挖掘使用正则表达式 (RE),包括机器生成的RE.
- 临床诊断标准被转化为可编程疾病诊断模型.
- 来自400个中国生殖EMR的数据使用五倍交叉验证进行了分析.
主要成果:
- 机器生成的RE显示了与手工制造的RE相当的性能 (F评分为0.9444).
- 该NLP提取器准确地识别了激素水平和超声波变化 (F-分数>0.95).
- 诊断模型在风险查中实现了0.98准确度和0.93灵敏度.
- 自动诊断与临床诊断的一致性为76% (kappa=0.63).
结论:
- RegEMR是一种中国的NLP系统,用于自动识别早期卵巢衰老风险和通过EMR诊断疾病.
- 该系统可以协助基于EMR的数据收集和生育中心的临床决策支持.
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