利用自然语言处理来识别患有 status epilepticus 的儿科患者.
Molly Ann Puckett1, Fatemeh Mohammad Alizadeh Chafjiri1, Jennifer V Gettings1
1Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, USA.
Seizure
|January 12, 2025
概括
与手动审查相比,自然语言处理 (NLP) 工具,如文档审查工具 (DrT) 在电子健康记录 (EHR) 中显著改善了在电子健康记录 (EHR) 中识别已确定的状态 (ESE) 和耐火状态 (RSE) 的患者.
科学领域:
- 医疗信息学 医疗信息学
- 神经学 神经学
- 医疗保健中的人工智能
背景情况:
- 电子健康记录 (EHR) 对临床研究和患者护理至关重要.
- 从电子健康记录中识别患有特定神经疾病的患者,如既定状态 (ESE) 和耐火状态 (RSE),可能是具有挑战性和耗时的.
- 目前的方法通常依赖于手动图表审查,这可能受到资源限制和人为错误的可能性的限制.
研究的目的:
- 为了比较自然语言处理 (NLP) 的有效性,辅助审查与传统的人类审查用于在EHR中识别ESE和RSE患者.
- 评估预先训练的NLP工具 (文档审查工具 - DrT) 在检测这些特定患者队列中的灵敏度和准确性.
- 评估NLP工具的潜力,以提高患者识别的研究和临床应用.
主要方法:
- 利用波士顿儿童医院 (BCH) 的儿科患者 (1个月至21岁) 的EHR数据.
- 采用预先训练的NLP工具 (DrT),利用机器学习 (SVM和bag-of-n-grams) 来识别患有性ESE或RSE的患者.
- 将DrT识别的病例与儿科病态发症研究小组 (pSERG) 财团的人类审查笔记的黄金标准进行了比较.
主要成果:
- 与人体审查相比,DrT在识别RSE (98.8%) 和ESE (99.5%) 的患者方面表现出显著更高的灵敏度 (RSE为67.4%,ESE为43.8%).
- 医疗评估发现的RSE患者数量 (170 vs. 116) 和ESE患者数量 (207 vs. 91) 大大超过了人类评估.
- 虽然DrT错过了3个病例,但它确定了173个通过手动审查无法发现的额外病例,突出了其全面的检测能力.
结论:
- 使用DrT进行NLP辅助审查,在识别ESE和RSE患者方面显著优于标准人体审查.
- 医疗疗技术提供了一种更灵敏,更有效的方法,用于从EHR数据中识别患者队列.
- 像DRT这样的NLP工具在资源有限的环境中是非常宝贵的,可以改善患者鉴定研究,治疗方案和预防性护理.
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