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机器学习模型和人类专业知识对护理干预分类的比较分析

Jerome Niyirora^1,2, Lynne Longtin¹, Cynthia Grabski^1,2

¹College of Health Sciences, SUNY Polytechnic Institute, Utica, NY 13502, United States.

|June 30, 2025

概括

机器学习模型显示了分类护理干预的潜力,但不能完全取代人类的专业知识. 复杂的,取决于上下文的临床文档需要进一步开发.

科学领域:

临床信息学临床信息学
医疗保健中的人工智能
护理文档护理文档

背景情况:

护理笔记的自动分类对于标准化数据和基准测试至关重要.
护理干预分类 (NIC) 系统为护理护理提供了一个标准化的框架.

研究的目的:

将机器学习 (ML) 模型与人类专家的性能进行比较,将护理笔记映射到NIC系统中.
识别ML模型在临床文档分类中出色的领域,以及在临床文档分类中落后的领域.

主要方法:

开发和评估了四种ML模型:TF-IDF,UMLS语义映射,GPT-4o mini和生物临床BERT.
使用了一组数据集,该数据集是未识别的家庭医疗保健护理笔记.
使用协议统计,精度,回忆,F1分数和科恩的卡帕评估绩效,将ML模型与专家护士分类进行比较.

主要成果:

人类专家证明了比ML模型更高的同意和F1分数.
在机器学习模型中,GPT-4o mini实现了最佳性能,但仍然落后于人类专家.
机器学习模型在常见的,明确定义的干预措施 (例如药物管理) 上表现良好,但在细微的,取决于上下文的干预措施 (例如信息管理) 上表现不佳.

结论:

当前的ML模型可以帮助,但不能完全取代复杂的护理干预分类的人类判断.
改进的ML方法是必要的,以准确地捕捉临床术语和特定背景文档的细微差别.

关键词:

机器学习是机器学习.自然语言处理自然语言处理.护理干预是指护理干预.语义映射是一个语义映射.文字分类文本分类文本分类

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