自然语言处理 - - 一个监控的基石,用于识别虐待儿童的行为
May Shum1, Allen Hsiao1, Wei Teng2
1Department of Pediatrics (M Shum, A Hsiao, A Asnes, and G Tiyyagura), Yale University School of Medicine, New Haven, Conn.
Academic pediatrics
|August 31, 2023
概括
对儿童虐待伤害进行自然语言处理 (NLP) 算法的改进提高了它的准确性. 实时临床决策支持 (CDS) 工具可以帮助紧急服务提供者识别和评估潜在的滥用案例.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 儿童虐待检测 儿童虐待的检测
背景情况:
- 虐待儿童的伤害需要在紧急情况下及时准确地识别.
- 现有的识别虐待相关伤害的方法可能是不一致的.
- 自然语言处理 (NLP) 提供了对临床笔记的自动化分析的潜力.
研究的目的:
- 改进一种NLP算法,用于识别与虐待儿童有关的伤害.
- 确定将NLP整合到实时临床决策支持 (CDS) 工具中的机会.
- 改善在急诊室 (ED) 中对潜在的虐待儿童案件的识别和评估.
主要方法:
- 在"静音模式"中,对9个ED的353个ED提供者笔记应用了一种NLP算法.
- 算法触发器得到了改进,以提高特异性和减少错误的阳性.
- 评估了对临床指南的遵守以及评估/报告中的人口差异.
主要成果:
- 由于解释错误,73个案例错误地触发了NLP;改进提高了具体性.
- 遵守推的评估标准为63%.
- 根据ED类型,保险和种族/种族,在评估和报告中观察到显著的人口差异.
结论:
- 在静音模式下NLP算法的改进提高了检测儿童虐待伤害的准确性.
- 实时CDS集成可以帮助ED提供者识别和评估与儿童身体虐待相关的伤害.
- 解决人口差异对于公平的护理至关重要.
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