Natural language processing and its application in the medical field_Chinese Journal of Evidence-Based Medicine

Authors：

HU Jiayuan ¹ , QIU Ruijin ² , SUN Yang ² ,  SHANG Hongcai ²

1. Beijing Hospital of Traditional Chinese Medicine Affiliated to Capital Medical University, Beijing 100010, P. R. China;
2. Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, P. R. China;

Corresponding author：

SHANG Hongcai, Email: shanghongcai@126.com

Keywords：

Natural language processing; Medicine information; Traditional Chinese medicine

DOI：

10.7507/1672-2531.202311178

Video：

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Natural language processing (NLP) is the embodiment of computer intelligence in acquiring knowledge, understanding, processing and expressing consciously and actively. It is the scientific key to promoting the informatization of medical practice and research. This paper reviews the development history and research basis of NLP, and focuses on the current application of NLP and large language models in biomedicine and traditional Chinese medicine (TCM), including the intelligent reading, information extraction and feedback of medical texts and ancient books of TCM, as well as the construction of medical knowledge graph and question-answering system. NLP is the technical support to explore the treasure house of TCM, which is of great practical significance to further promote the development of efficient and high-quality core values of TCM and to improve the service capacity.

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2. 冯志伟. 自然语言处理的历史与现状. 中国外语, 2008, (1): 14-22.
3. 李宇明. 语言学是一个学科群. 语言战略研究, 2018, 3(1): 15-24.
4. Booth AD, Brandwood L, Cleave JP. Mechanical resolution of linguistic problems. 1958.
5. Searle JR. Minds, brains, and programs. Behav Brain Sci, 1980, 3(3): 417-457.
6. 王大鹏. 国内语料库发展现存问题与分析. 渤海大学学报(哲学社会科学版), 2010, 32(3): 137-140.
7. 詹卫东, 郭锐, 常宝宝, 等. 北京大学CCL语料库的研制. 语料库语言学, 2019, 6(1): 71-86, 116.
8. 北京大学开放研究数据平台. 北京大学计算语言学研究所: 综合型语言知识库(CLKB).
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19. Pang BLL. Opinion mining and sentiment analysis. Found Trend Inform Retriev, 2008, 2(1-2): 1-135.
20. Hassan A, Mahmood A. Efficient deep learning model for text classification based on recurrent and convolutional layers. 2017.
21. Balaneshinkordan S, Kotov A. Bayesian approach to incorporating different types of biomedical knowledge bases into information retrieval systems for clinical decision support in precision medicine. J Biomed Inform, 2019, 98: 103238.
22. 吴友政. 构建汉语问答系统评测平台. 2004.
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24. Moirangthem DS, Lee M. Abstractive summarization of long texts by representing multiple compositionalities with temporal hierarchical pointer generator network. Neural Netw, 2020, 124: 1-11.
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28. 欧阳恩, 李作高, 李昱熙, 等. 基于深度学习的电子病历命名实体识别及其在知识发现中的应用. 中国卫生信息管理杂志, 2018, 15(4): 469-473.
29. 易应萍, 张志强, 王强. 基于自然语言处理技术的医学命名实体解析研究. 中国数字医学, 2018, 13(12): 20-22.
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36. Valtchinov VI, Lacson R, Wang A, et al. Comparing artificial intelligence approaches to retrieve clinical reports documenting implantable devices posing MRI safety risks. J Am Coll Radiol, 2020, 17(2): 272-279.
37. Verma AA, Masoom H, Pou-Prom C, et al. Developing and validating natural language processing algorithms for radiology reports compared to ICD-10 codes for identifying venous thromboembolism in hospitalized medical patients. Thromb Res, 2022, 209: 51-58.
38. Shelmerdine SC, Singh M, Norman W, et al. Automated data extraction and report analysis in computer-aided radiology audit: practice implications from post-mortem paediatric imaging. Clin Radiol, 2019, 74(9): 733.
39. Morandini P, Laino ME, Paoletti G, et al. Artificial intelligence processing electronic health records to identify commonalities and comorbidities cluster at Immuno Center Humanitas. Clin Transl Allergy, 2022, 12(6): e12144.
40. Aakre CA. Applying natural language processing neural network architectures to augment appointment request review of self-referred patients to an academic medical center. AMIA Annu Symp Proc, 2022, 2022: 85-91.
41. Lee KC, Udelsman BV, Streid J, et al. Natural language processing accurately measures adherence to best practice guidelines for palliative care in Trauma. J Pain Symptom Manage, 2020, 59(2): 225-232.
42. Hussain SA, Sezgin E, Krivchenia K, et al. A natural language processing pipeline to synthesize patient-generated notes toward improving remote care and chronic disease management: a cystic fibrosis case study. JAMIA Open, 2021, 4(3): b84.
43. Weitzman ER, Magane KM, Chen PH, et al. Online searching and social media to detect alcohol use risk at population scale. Am J Prev Med, 2020, 58(1): 79-88.
44. 聂莉莉, 李传富, 许晓倩, 等. 人工智能在医学诊断知识图谱构建中的应用研究. 医学信息学杂志, 2018, 39(6): 7-12.
45. 赵雪娇. 妇产科知识图谱构建研究与实现. 中国数字医学, 2019, 14(1): 3-5.
46. 张小亮, 王忠民, 王永庆, 等. 基于自然语言处理的临床合理用药知识图谱构建. 中华医学图书情报杂志, 2019, 28(9): 1-5.
47. 王琦, 康亮环, 刘国臻. 基于临床知识图谱的慢性肾病辅助决策模型研究. 邮电设计技术, 2018, (12): 68-71.
48. 黄梦禧, 张青川, 陈龙, 等. 面向医学领域的智能问答APP设计与实现. 软件导刊, 2019, 18(3): 94-99.
49. Demner-Fushman D, Mrabet Y, Ben Abacha A. Consumer health information and question answering: helping consumers find answers to their health-related information needs. J Am Med Inform Assoc, 2020, 27(2): 194-201.
50. Legrand J, Gogdemir R, Bousquet C, et al. PGxCorpus, a manually annotated corpus for pharmacogenomics. Sci Data, 2020, 7(1): 3.
51. Kang MJ, Dykes PC, Korach TZ, et al. Identifying nurses' concern concepts about patient deterioration using a standard nursing terminology. Int J Med Inform, 2020, 133: 104016.
52. 朱明宇. 基于医学人工智能技术的病案首页智能编码研究. 中国数字医学, 2018, 13(4): 34-36.
53. Fujimori R, Liu K, Soeno S, et al. Acceptance, barriers, and facilitators to implementing artificial intelligence-based decision support systems in emergency departments: quantitative and qualitative evaluation. JMIR Form Res, 2022, 6(6): e36501.
54. 刘广建, 李晓君, 李庆丰, 等. 临床表型数据和医学知识驱动的儿童脓毒症亚型识别. 中国数字医学, 2019, 14(3): 66-69.
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