文本挖掘用于兽医临床数据中的疾病监测:第二部分,训练计算机识别临床文本中的特征
Heather Davies1, Goran Nenadic2, Ghada Alfattni2
1Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, United Kingdom.
Frontiers in veterinary science
|September 6, 2024
概括
机器学习工具为大型数据集自动化兽医临床文本挖掘. 研究人员和临床医生必须合作,以确保人工智能模型的输出可以解释和可靠.
科学领域:
- 兽医信息学是一门兽医信息学.
- 机器学习应用程序 机器学习应用程序
- 自然语言处理自然语言处理.
背景情况:
- 来自SAVSNET和VetCompass等项目的大型兽医临床数据集需要有效的数据提取.
- 传统的文本挖掘方法对于复杂的临床叙述是不够的.
- 机器学习为兽医临床文本分析提供了先进的解决方案.
研究的目的:
- 评估用于兽医临床文本挖掘的现有机器学习工具.
- 讨论各种机器学习技术在这个领域的应用.
- 突出复杂的人工智能模型中可解释性的重要性.
主要方法:
- 审查机器学习技术,从简单的词汇扩展到复杂的语言模型.
- 用于记录注释和无监督主题建模的语言模型的描述.
- 讨论最近的进展,包括像ChatGPT这样的生成模型.
主要成果:
- 机器学习工具可以自动从广泛的兽医临床叙述中提取信息.
- 各种ML技术,包括生成模型,适用于兽医文本数据.
- 复杂的人工智能模型的解释性对于建立对研究结果的信心至关重要.
结论:
- 机器学习对于处理大型兽医临床数据至关重要.
- 研究人员和临床医生之间的合作对于验证人工智能驱动的见解至关重要.
- 确保模型可解释性将促进对兽医人工智能的信任和采用.
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