机器学习用于耳鼻喉科住院预测分析的推信
Vikram Vasan1, Christopher P Cheng1, David K Lerner1,2
1Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, U.S.A.
The Laryngoscope
|April 11, 2024
概括
机器学习模型可以使用推信 (LOR) 预测耳鼻喉科住院面试邀请. 自然语言处理技术有效地从LOR文本中提取有价值的见解,用于申请人选.
科学领域:
- 医学教育 医学教育
- 人工智能在医学中的应用
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 推信 (LOR) 对于入住医院的录取至关重要,但也是主观的.
- 本研究探讨了LOR内容对申请人结果的预测能力.
- 耳鼻喉科住院的选择是过程改进的一个关键领域.
研究的目的:
- 调查自然语言处理 (NLP) 和机器学习 (ML) 在预测耳鼻喉科住院面试邀请的有效性.
- 确定LOR文本是否可以提供对申请人适度的可量化的见解.
- 评估自动化工具在申请居留权审查中的潜力.
主要方法:
- 从2022-2023年耳鼻喉科应用周期对1642个LOR进行了回顾性分析.
- 使用CountVectorizer (CV),术语频率-反向文档频率 (TF-IDF) 和Word2Vec (WV) 的文字预处理和向量化.
- 培训和测试五种ML模型 (逻辑回归,天真贝叶斯,决策树,随机森林,支持矢量机) 来预测面试邀请.
主要成果:
- 该研究分析了337名申请人的1642个LOR,其中67人接受了采访.
- 决策树模型,特别是使用TF-IDF和CV向量化的模型,在预测面试邀请方面表现最好.
- 这些模型在从LORs分类有意义的信息时实现了比机会更好的准确性.
结论:
- 机器学习模型与文本向量化相结合,可以有效地预测耳鼻喉科住院面试邀请.
- NLP和ML提供了一种有希望的方法,可以从主观的LOR中提取客观见解.
- 自动预测工具可以提高居住计划选择流程的效率和客观性.
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