自然语言处理方法和应用在甲状腺学中的系统审查
Ricardo Loor-Torres1, Mayra Duran1, David Toro-Tobon1
1Knowledge and Evaluation Research Unit (R.L.-T., M.D., M.M.C., C.S.., D.S.T., S.A.P., V.M., E.G., M.B.O., J.P.B.), Division of Endocrinology, Diabetes, Metabolism, and Nutrition (D.T.-T., J.P.B.), Department of Medicine, and Department of Artificial Intelligence and Informatics (N.S.O.), Mayo Clinic, Rochester, MN; University of Edinburgh, Edinburgh, Scotland, United Kingdom (D.S.T.); Montefiore Health Center, Albert Einstein College of Medicine, New York, NY (J.W.F.); Division of Endocrinology, Department of Medicine (N.S.O.), and Department of Health Outcomes and Biomedical Informatics (Y.W.), University of Florida, Gainesville, FL; and Respiratory, Cardiovascular, and Renal Pathobiology and Bioengineering, Universitat de Barcelona, Spain (D.S.T.).
自然语言处理 (NLP) 在甲状腺学中越来越多地使用,特别是在甲状腺结节和癌症中,深度学习方法占主导地位. 然而,由于文档和可移植性挑战,NLP工具尚未进入临床实践.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 内分泌学 在内分泌学.
背景情况:
- 自然语言处理 (NLP) 为分析甲状腺学中复杂的医疗数据提供了潜力.
- 现有的研究已经探索了与甲状腺有关的各种NLP技术.
研究的目的:
- 在甲状腺疾病中系统审查NLP应用.
- 总结当前的挑战和未来的方向在甲状腺学NLP.
主要方法:
- 在甲状腺疾病中NLP应用的系统文献搜索 (2012-2023年).
- 提取NLP方法,应用,条件,数据源和性能指标.
- 雪球技术,以确定其他相关研究.
主要成果:
- 确定了24项符合条件的NLP研究,其中深度学习是最常见的方法 (38%).
- 甲状腺结节 (54%) 和甲状腺癌 (29%) 是主要研究的疾病,主要使用电子健康记录 (71%).
- 尽管深度学习的兴趣和使用越来越多,但没有NLP应用达到临床实践,理由是有限的外部验证.
结论:
- 甲状腺学中的NLP应用正在进步,特别是对于结节和癌症,深度学习显示出有前途.
- 仍然存在重大挑战,包括不一致的临床文档和模型可移植性,阻碍了临床实施.
- 需要进一步的研究和开发,以弥合NLP研究和甲状腺护理中的实际临床应用之间的差距.
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