改善了护理和老年护理中句子建议的评估指标记录应用程序
Defry Hamdhana1,2, Haru Kaneko1, John Noel Victorino1
1Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Kitakyushu 808-0196, Japan.
一个新的评估模型,EmbedHDP,改善了护理记录中的句子建议. 它与人类护理人员的协调性更好,而不是现有的指标,如BERTScore,等号相似性,ROUGE和BLUE.
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.
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 目前护理护理记录的评估模型有局限性.
- 现有的指标如BERTScore,cosine相似性,ROUGE和BLEU与护理领域特定的细微差别和语义准确性作斗争.
研究的目的:
- 介绍EmbedHDP,一个新的评估方法,护理护理记录句子建议.
- 评估EmbedHDP与人类护理人员评估的一致性.
- 在评估医疗保健相关文本时克服现有指标的局限性.
主要方法:
- 分析现有指标 (BERTScore,等号相似性,ROUGE,BLEU) 和它们的局限性.
- 开发和应用EmbedHDP评估模型.
- 使用320个句子对的数据集计算相关系数.
主要成果:
- 与其他模型相比,EmbedHDP在评估护理记录方面表现优越.
- 在EmbedHDP中,系数得分为61%,超过了同位素相似性 (59%) 和BERTScore (58%).
- 该模型有效地处理各种各样的句子结构和医学术语.
结论:
- 在护理护理应用中,EmbedHDP提供了一种更有效,更符合情境的方法来评估句子建议.
- 提出的方法在改善医疗保健机构自动化评估的质量和可靠性方面显著有前途.
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