护理工作量:使用人工智能开发分类模型
Ninon Girardon da Rosa1,2, Tiago Andres Vaz3, Amália de Fátima Lucena1,4,5
1Universidade Federal do Rio Grande do Sul, Escola de Enfermagem, Porto Alegre, RS, Brazil.
一个新的人工智能模型使用电子健康记录预测护理工作量. 这种人工智能工具可以有效地自动化护理工作量评估,提高医疗保健的效率.
科学领域:
- 护理信息学 护理信息学
- 医疗保健中的人工智能
- 机器学习应用 机器学习应用
背景情况:
- 准确的护理工作量评估对于最佳的患者护理和资源分配至关重要.
- 传统的护理工作量评估方法可能是耗时和主观的.
- 人工智能的集成为更客观,更有效的工作负载预测提供了潜力.
研究的目的:
- 开发和验证一个预测性护理工作负载分类模型.
- 利用人工智能 (AI) 和机器学习 (ML) 来预测工作负载.
- 从电子病历中识别出预测护理工作负载的关键变量.
主要方法:
- 使用电子病历进行的回顾性观察性研究.
- 机器学习算法应用于43871个护理评估和11774个患者记录的数据集.
- 在Dataiku®数据科学平台上进行数据分析,用于探索性,描述性和预测性分析.
主要成果:
- 成功开发了一种用于护理工作负载的支持人工智能的分类模型.
- 该模型在分类有助于工作负载预测的变量方面实现了72%的准确性.
- 接收器操作特征曲线下的面积为82%,表明了良好的预测性能.
结论:
- 通过使用电子健康记录数据来训练人工智能算法来预测护理工作量是可行的.
- 人工智能工具在自动化护理工作量评估过程中表现出有效性.
- 这种预测模型有可能提高医疗保健资源管理的效率和准确性.
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