Improving Neurology Clinical Care With Natural Language Processing Tools

被引:2
|
作者
Ge, Wendong [1 ]
Rice, Hunter J. [1 ]
Sheikh, Irfan S. [1 ]
Westover, M. Brandon [2 ]
Weathers, Allison L. [3 ]
Jones, Lyell K. [4 ]
Moura, Lidia [1 ,5 ]
机构
[1] Massachusetts Gen Hosp, Dept Neurol, Boston, MA 02114 USA
[2] Beth Israel Deaconess Med Ctr, Dept Neurol, Boston, MA USA
[3] Cleveland Clin, Informat Technol Div, Cleveland, OH USA
[4] Mayo Clin, Dept Neurol, Rochester, MN USA
[5] Harvard Med Sch, Dept Neurol, Boston, MA 02138 USA
关键词
D O I
10.1212/WNL.0000000000207853
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
The integration of natural language processing (NLP) tools into neurology workflows has the potential to significantly enhance clinical care. However, it is important to address the limitations and risks associated with integrating this new technology. Recent advances in transformer-based NLP algorithms (e.g., GPT, BERT) could augment neurology clinical care by summarizing patient health information, suggesting care options, and assisting research involving large datasets. However, these NLP platforms have potential risks including fabricated facts and data security and substantial barriers for implementation. Although these risks and barriers need to be considered, the benefits for providers, patients, and communities are substantial. With these systems achieving greater functionality and the pace of medical need increasing, integrating these tools into clinical care may prove not only beneficial but necessary. Further investigation is needed to design implementation strategies, mitigate risks, and overcome barriers.
引用
收藏
页码:1010 / 1018
页数:9
相关论文
共 50 条
  • [31] Processing natural language without natural language processing
    Brill, E
    COMPUTATIONAL LINGUISTICS AND INTELLIGENT TEXT PROCESSING, PROCEEDINGS, 2003, 2588 : 360 - 369
  • [32] CLINICAL DECISION SUPPORT TOOLS IMPROVING CANCER CARE
    Stillman, Robert C.
    SEMINARS IN ONCOLOGY NURSING, 2018, 34 (02) : 158 - 167
  • [33] Natural Language Processing techniques for researching and improving peer feedback
    Xiong, Wenting
    Litman, Diane
    Schunn, Christian
    JOURNAL OF WRITING RESEARCH, 2012, 4 (02) : 155 - 176
  • [34] Improving Neural Models for Natural Language Processing in Russian with Synonyms
    Galinsky R.B.
    Alekseev A.M.
    Nikolenko S.I.
    Journal of Mathematical Sciences, 2023, 273 (4) : 583 - 594
  • [35] Improving humanitarian needs assessments through natural language processing
    Kreutzer, T.
    Vinck, P.
    Pham, P. N.
    An, A.
    Appel, L.
    DeLuca, E.
    Tang, G.
    Alzghool, M.
    Hachhethu, K.
    Morris, B.
    Walton-Ellery, S. L.
    Crowley, J.
    Orbinski, J.
    IBM JOURNAL OF RESEARCH AND DEVELOPMENT, 2020, 64 (1-2)
  • [36] Natural Language Processing Tools for Romanian - Going Beyond a Low-Resource Language
    Nitu, Melania
    Dascalu, Mihai
    INTERACTION DESIGN AND ARCHITECTURES, 2024, (60) : 7 - 26
  • [37] Automated Summarization Evaluation (ASE) Using Natural Language Processing Tools
    Crossley, Scott A.
    Kim, Minkyung
    Allen, Laura
    McNamara, Danielle
    ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2019), PT I, 2019, 11625 : 84 - 95
  • [38] Evaluating Natural Language Processing tools for Polish during PolEval 2019
    Kobylinski, Lukasz
    Ogrodniczuk, Maciej
    Kocon, Jan
    Marcinczuk, Michal
    Smywinski-Pohl, Aleksander
    Wolk, Krzysztof
    Korzinek, Danijel
    Ptaszynski, Michal
    Pieciukiewicz, Agata
    Dybala, Pawel
    HUMAN LANGUAGE TECHNOLOGY: CHALLENGES FOR COMPUTER SCIENCE AND LINGUISTICS, LTC 2019, 2022, 13212 : 303 - 321
  • [39] An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools
    Lauriola, Ivano
    Lavelli, Alberto
    Aiolli, Fabio
    NEUROCOMPUTING, 2022, 470 : 443 - 456
  • [40] Information retrieval in digital theses based on natural language processing tools
    Abascal, R
    Rumpler, A
    Pinon, JM
    ADVANCES IN NATURAL LANGUAGE PROCESSING, 2004, 3230 : 172 - 182