数字技术在病理学家工作中的应用：使用自动语音识别系统培训的研究

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅或者付费存取

详细

自然语言处理，或Natural language processing是计算语言学的一个领域。这是计算机科学的一个分支，包括自然语言中的语音和文本的算法处理。利用这些算法，机器翻译系统、问题答案和数字自动语音识别应运而生。数字自动语音识别的应用主要是在人机交互过程中进行语音转换，生成相关且有意义的文本，以及用自然语言进行交流。如今，这些系统被广泛应用于医学实践，包括病理解剖学。成功使用自动语音识别系统的主要步骤如下：编写标准模板（用于自动纳入诊断的描述），并培训医生在实践中使用此类系统的技能。长期以来，世界各地的医生都试图将病理解剖学结论标准化。通过对国内外文献的研究，我们编制了宏观描述和微观描述中必须包含的元素列表，并在最终结论中体现出来。这些模板有助于作出决定和准确地作出诊断，因为它们按照重要性的顺序包含所有关键因素。这大大减少了对固定宏观材料和额外组织学切片的重复检查。内置在数字自动语音识别中的模板可以减少维护文档的时间，并显著减少病理学家的工作量。为成功使用数字自动语音识别，我们为国内外博士研究生开设了《解剖病理学实践中的数字语音识别》培训课程。这篇文章给出了课程的简要描述，课程本身可以在互联网上找到。

全文:

受限制的访问

作者简介

Andrey Khramtsov

Ann & Robert H. Lurie Children’s Hospital of Chicago

编辑信件的主要联系方式.
Email: duvip@yandex.ru

MD, PhD, Senior Researcher, Department of Pathology and Laboratory Medicine

美国, Chicago

Ruslan Nasyrov

Saint Petersburg State Pediatric Medical University of the Ministry of Healthcare of the Russian Federation

Email: ran.53@mail.ru

MD, PhD, Dr. Sci. (Med.), Professor, Head, Department of Anatomic Pathology and Forensic Medicine

俄罗斯联邦, Saint Petersburg

Galina Khramtsova

The University of Chicago

Email: galina@uchicago.edu

MD, PhD, Senior Researcher, Department of Medicine, Section of Hematology and Oncology

美国, Chicago

参考

  1. Белоногов Г.Г. Компьютерная лингвистика и перспективные информационные технологии. М.: Русский мир, 2004. [Belonogov GG. Komp’yuternaya lingvistika i perspektivnye informatsionnye tekhnologii. Moscow: Russkiy mir; 2004. (In Russ.)]
  2. Боярский К.К. Введение в компьютерную лингвистику. Учеб. пос. СПб.: НИУ ИТМО, 2013. [Boyarskiy KK. Vvedenie v komp’yuternuyu lingvistiku. Ucheb. pos. Saint Petersburg: NIU ITMO; 2013. (In Russ.)]
  3. Кудрявцев Н.Д., Сергунова К.А., Иванова Г.В., и др. Оценка эффективности внедрения технологии распознавания речи для подготовки протоколов рентгенологических исследований//Врач и информационные технологии. 2020. Т. S1. С. 58–64. [Kudryavtsev ND, Sergunova KA, Ivanova GV, et al. Evolution of the effectiveness of the introduction of speech recognition technology for the preparation of protocols for X-ray examinations. Information Technologies for the Physician. 2020; S1:58-64. (In Russ.)] doi: 10.37690/1811-0193-2020-S1-58-64
  4. Мальков П.Г., Франк Г.А., Пальцев М.А. Стандартные технологические процедуры при проведении патологоанатомических исследований: Клинические рекомендации. М.: Практическая медицина, 2017. [Mal’kov PG, Frank GA, Pal’tsev MA. Standartnye tekhnologicheskie protsedury pri provedenii patologoanatomicheskikh issledovaniy: Klinicheskie rekomendatsii. Moscow: Prakticheskaya meditsina; 2017. (In Russ.)]
  5. Трякин А. А., Гладков О. А., Матвеев В. Б., и др. Практические рекомендации по лечению герминогенных опухолей у мужчин. Злокачественные опухоли: Практические рекомендации//RUSSCO. 2020. T. 10, № 3s2. С. 572–602. [Tryakin AA, Gladkov OA, Matveev VB, et al. Prakticheskie rekomendatsii po lecheniyu germinogennykh opukholey u muzhchin Zlokachestvennye opukholi: Prakticheskie rekomendatsii. RUSSCO. 2020;10(3s2):572-602. (In Russ.)] doi: 10.18027/2224-5057-2020-10-3s2-34
  6. Цитульский А.М., Иванников А.В., Рогов И.С. NLP — Обработка естественных языков//Научно-образовательный журнал для студентов и преподавателей StudNet. 2020. Т. 3. № 6. С. 467–475. [Tsitul’skiy AM, Ivannikov AV, Rogov IS. NLP – Natural language processing. StudNet. 2020;6:467-475. (In Russ.)]
  7. Blackley SV, Schubert VD, Goss FR, et al. Physician use of speech recognition versus typing in clinical documentation: A controlled observational study. Int J Med Inform. 2020;141:104178. doi: 10.1016/j.ijmedinf.2020.104178
  8. Goldsmith JD, Siegal GP, Suster S, et al. Reporting guidelines for clinical laboratory reports in surgical pathology. Arch Pathol Lab Med. 2008;132(10):1608-1616. doi: 10.5858/2008-132-1608-RGFCLR
  9. Henricks WH, Roumina K, Skilton BE, et al. The utility and cost effectiveness of voice recognition technology in surgical pathology. Mod Pathol. 2002;15(5):565-571. doi: 10.1038/modpathol.3880564
  10. Kang HP, Sirintrapun SJ, Nestler RJ, Parwani AV. Experience with voice recognition in surgical pathology at a large academic multi-institutional center. Am J Clin Pathol. 2010;133(1):156-159. doi: 10.1309/AJCPOI5F1LPSLZKP
  11. Renshaw AA, Mena-Allauca M, Gould EW, Sirintrapun SJ. Synoptic Reporting: Evidence-Based Review and Future Directions. JCO Clin Cancer Inform. 2018;2:1-9. doi: 10.1200/CCI.17.00088
  12. Singh M, Pal TR. Voice recognition technology implementation in surgical pathology: advantages and limitations. Arch Pathol Lab Med. 2011;135(11): 1476-1481. doi: 10.5858/arpa.2010-0714-OA
  13. Sluijter CE, van Workum F, Wiggers T, et al. Improvement of Care in Patients with Colorectal Cancer: Influence of the Introduction of Standardized Structured Reporting for Pathology. JCO Clin Cancer Inform. 2019;3:1-12. doi: 10.1200/CCI.18.00104
  14. Valenstein PN. Formatting pathology reports: applying four design principles to improve communication and patient safety. Arch Pathol Lab Med. 2008;132(1): 84-94. doi: 10.5858/2008-132-84-FPRAFD
  15. Veras LV, Arnold M, Avansino JR, et al. Guidelines for synoptic reporting of surgery and pathology in Hirschsprung disease. J Pediatr Surg. 2019;54(10):2017-2023. doi: 10.1016/j.jpedsurg.2019.03.010
  16. Программа Voice2Med. Режим доступа: https://www.speechpro.ru/product/programmy-dlya-raspoznavaniya-rechi-v-tekst/voice2med Дата обращения: 06.09.21. [Program Voice2Med. Available from: https://www.speechpro.ru/product/programmy-dlya-raspoznavaniya-rechi-v-tekst/voice2med (In Russ.)]
  17. Digital Speech Recognition Systems in an Anatomical Pathology Practice. Media Lab. Available from: https://www.medialab.com/digital_speech_recognition_systems_in_an_anatomic_pathology_practice.aspx Accessed: 06.09.2021.
  18. Dragon Medical One. Nuance Communications Inc. Available from: https://www.nuance.com/healthcare/provider-solutions/speech-recognition/dragon-medical-one.html Accessed: 06.09.21.
  19. VoiceOver PRO. Voicebrook Inc. Available from: https://www.voicebrook.com/voiceover.
  20. The College of American Pathologists (CAP) eCC (electronic Cancer Checklists). Available from: https://www.cap.org/laboratory-improvement/proficiency-testing/cap-ecc
  21. The Say it for Pathologists. Inc n Voq. Available from: https://sayit.nvoq.com/medical-dictation-software-for-pathologists/ (accessed 07.03.2021).
  22. Fusion Speech for Pathology. Dolbey. Available from: https://www.dolbey.com/solutions/speech-recognition/fusion-speechemr/pathology/(accessed 07.03.2021).

补充文件

附件文件
动作
1. JATS XML
下载
2. 图. 1. 数字自动语音识别的工作原理 Fig. 1. General architecture of digital speech recognition systems

下载 (53KB)
索引源数据
3. 图. 2. 使用Dragon Medical One软件创建宏观描述和组织学诊断协议的一个例子 Fig. 2. An example of work with Dragon Medical One software for dictation of gross description and histological diagnosis

下载 (251KB)
索引源数据

版权所有 © Khramtsov A., Nasyrov R., Khramtsova G., 2021

Creative Commons License
此作品已接受知识共享署名 4.0国际许可协议的许可
СМИ зарегистрировано Федеральной службой по надзору в сфере связи, информационных технологий и массовых коммуникаций (Роскомнадзор).
Регистрационный номер и дата принятия решения о регистрации СМИ: серия ПИ № ФС 77 - 69634 от 15.03.2021 г.


##common.cookie##