Skip to main content

Advertisement

SpringerLink
Log in

Natural language processing: using artificial intelligence to understand human language in orthopedics

  • Review
  • Published:
Knee Surgery, Sports Traumatology, Arthroscopy Aims and scope

Abstract

Natural language processing (NLP) describes the broad field of artificial intelligence by which computers are trained to understand and generate human language. Within healthcare research, NLP is commonly used for variable extraction and classification/cohort identification tasks. While these tools are becoming increasingly popular and available as both open-source and commercial products, there is a paucity of the literature within the orthopedic space describing the key tasks within these powerful pipelines. Curation and navigation of the electronic medical record are becoming increasingly onerous, and it is important for physicians and other healthcare professionals to understand potential methods of harnessing this large data resource. The purpose of this study is to provide an overview of the tasks required to develop an NLP pipeline for orthopedic research and present recent examples of successful implementations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Introducing the ONE Platform. https://cloudmedxhealth.com/product-solutions/one-platform/. Accessed 20 Oct, 2022

  2. Alsentzer E, Murphy JR, Boag W, Weng W-H, Jin D, Naumann T, et al. (2019) Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323

  3. Balakrishnan V, Ethe L (2014) Stemming and lemmatization: a comparison of retrieval performances. Lect Notes Softw Eng 2(3):262–267

    Article  Google Scholar 

  4. Ben-Ari A, Chansky H, Rozet I (2017) Preoperative opioid use is associated with early revision after total knee arthroplasty: a study of male patients treated in the veterans affairs system. J Bone Joint Surg Am 99:1–9

    Article  PubMed  Google Scholar 

  5. Brants T (2000) TnT-a statistical part-of-speech tagger. arXiv preprint cs/0003055

  6. Chen T, Guestrin C (2016) XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 2016

  7. CloudMedX (2022). The ONE Platform for Healthcare Brings it All Together https://cloudmedxhealth.com/wp-content/uploads/2022/05/CMX-ONE-platform-booklet-complete_V3.pdf. Accessed 15 Sep 2022

  8. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–2979

    Article  Google Scholar 

  9. Deanehan JK, Kimia AA, Tan Tanny SP, Milewski MD, Talusan PG, Smith BG et al (2013) Distinguishing Lyme from septic knee monoarthritis in Lyme disease-endemic areas. Pediatrics 131:e695-701

    Article  PubMed  Google Scholar 

  10. Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805

  11. Floyd JS, Heckbert SR, Weiss NS, Carrell DS, Psaty BM (2012) Use of administrative data to estimate the incidence of statin-related rhabdomyolysis. JAMA 307:1580–1582

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Friedl JE (2006) Mastering regular expressions. O’Reilly Media Inc, Sebastopol

    Google Scholar 

  13. Garla VN, Brandt C (2012) Ontology-guided feature engineering for clinical text classification. J Biomed Inform 45:992–998

    Article  PubMed  PubMed Central  Google Scholar 

  14. Géron A (2019) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media Inc, Sebastopol

    Google Scholar 

  15. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79:2554–2558

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Horev R (2018) BERT Explained: State of the art language model for NLP. https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270. Accessed 13 Sep 2022

  17. James G, Witten D, Hastie T, Tibshirani R (2021) An Introduction to Statistical Learning: with Applications in R. Springer, New York

    Book  Google Scholar 

  18. Jatnika D, Bijaksana MA, Suryani AA (2019) Word2vec model analysis for semantic similarities in english words. Procedia Comput Sci 157:160–167

    Article  Google Scholar 

  19. Jing L-P, Huang H-K, Shi H-B (2002). Improved feature selection approach TFIDF in text mining. Paper presented at: Proceedings of 2002 International Conference on Machine Learning and Cybernetics, Beijing, 4–9 December 2002

  20. Jurafsky D, Martin JH (2006) Speech and language processing: an introduction to natural language processing. Wiley, New York

    Google Scholar 

  21. Karhade AV, Bongers MER, Groot OQ, Cha TD, Doorly TP, Fogel HA et al (2020) Can natural language processing provide accurate, automated reporting of wound infection requiring reoperation after lumbar discectomy? Spine J 20:1602–1609

    Article  PubMed  Google Scholar 

  22. Khurana D, Koli A, Khatter K, Singh S (2022) Natural language processing: State of the art, current trends and challenges. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-13428-4

    Article  PubMed  PubMed Central  Google Scholar 

  23. Kimia AA, Savova G, Landschaft A, Harper MB (2015) An introduction to natural language processing: how you can get more from those electronic notes you are generating. Pediatr Emerg Care 31:536–541

    Article  PubMed  Google Scholar 

  24. Le Q, Mikolov T (2014). Distributed representations of sentences and documents. Paper presented at: 2014 International conference on machine learning, Beijing, 21–26 June 2014

  25. LeCun Y, Kavukcuoglu K, Farabet C (2010). Convolutional networks and applications in vision. Paper presented at: 2010 IEEE international symposium on circuits and systems, Paris, 30 May - 2 June 2010

  26. Lee J, Yoon W, Kim S, Kim D, Kim S, So CH et al (2020) BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36:1234–1240

    Article  CAS  PubMed  Google Scholar 

  27. Levin E, Pieraccini R, Eckert W (1998) Using Markov decision process for learning dialogue strategies. Paper presented at: 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, Seattle, 12–15 May 1998

  28. Liu G, Liao Y, Wang F, Zhang B, Zhang L, Liang X et al (2021) Medical-VLBERT: medical visual language BERT for COVID-19 CT report generation with alternate learning. IEEE Trans Neural Netw Learn Syst 32:3786–3797

    Article  PubMed  Google Scholar 

  29. Liu H, Bielinski SJ, Sohn S, Murphy S, Wagholikar KB, Jonnalagadda SR et al (2013) An information extraction framework for cohort identification using electronic health records. AMIA Jt Summits Transl Sci Proc b 2013:149

    Google Scholar 

  30. Lovins JB (1968) Development of a stemming algorithm. Mech Transl Comput Linguistics 11:22–31

    Google Scholar 

  31. Martin-Sanchez F, Verspoor K (2014) Big data in medicine is driving big changes. Year Med Inform 23:14–20

    Article  Google Scholar 

  32. Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781

  33. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Paper presented at: 27th Annual Conference on Neural Information Processing Systems, Lake Tahoe, 5–10 December 2013

  34. Müller AC, Guido S (2016) Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media Inc, Sebastopol

    Google Scholar 

  35. Nadkarni PM, Ohno-Machado L, Chapman WW (2011) Natural language processing: an introduction. J Am Med Inform Assoc 18:544–551

    Article  PubMed  PubMed Central  Google Scholar 

  36. Nair A (2021) Leveraging N-grams to Extract Context From Text. https://towardsdatascience.com/leveraging-n-grams-to-extract-context-from-text-bdc576b47049. Accessed 13 Sep, 2022

  37. Pruneski JA, Pareek A, Kunze KN, Martin RK, Karlsson J, Oeding JF et al (2022) Supervised machine learning and associated algorithms: applications in orthopedic surgery. Knee Surg Sports Traumatol Arthrosc. https://doi.org/10.1007/s00167-022-07181-2

    Article  PubMed  Google Scholar 

  38. Pruneski JA, Williams RJ 3rd, Nwachukwu BU, Ramkumar PN, Kiapour AM, Martin RK et al (2022) The development and deployment of machine learning models. Knee Surg Sports Traumatol Arthrosc. https://doi.org/10.1007/s00167-022-07155-4

    Article  PubMed  Google Scholar 

  39. Rai A, Borah S (2021) Study of various methods for tokenization. Applications of internet of things. Springer, Singapore, pp 193–200

    Chapter  Google Scholar 

  40. Rasmy L, Xiang Y, Xie Z, Tao C, Zhi D (2021) Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction. NPJ Digit Med 4:86

    Article  PubMed  PubMed Central  Google Scholar 

  41. Rothman D (2021) Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more. Packt Publishing Ltd, Birmingham

    Google Scholar 

  42. Sagheb E, Ramazanian T, Tafti AP, Fu S, Kremers WK, Berry DJ et al (2021) Use of natural language processing algorithms to identify common data elements in operative notes for knee arthroplasty. J Arthroplasty 36:922–926

    Article  PubMed  Google Scholar 

  43. Sanders TL, Pareek A, Desai VS, Hewett TE, Levy BA, Stuart MJ et al (2018) Low accuracy of diagnostic codes to identify anterior cruciate ligament tear in orthopedic database research. Am J Sports Med 46:2894–2898

    Article  PubMed  PubMed Central  Google Scholar 

  44. Shah RF, Bini S, Vail T (2020) Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients. Bone Joint J 102-B:99–104

    Article  PubMed  Google Scholar 

  45. Silva C, Ribeiro B (2003) The importance of stop word removal on recall values in text categorization. Paper presented at: International Joint Conference on Neural Networks, Istanbul, 26–29 June 2003

  46. Simha A. Understanding TF-IDF for Machine Learning (2021) https://www.capitalone.com/tech/machine-learning/understanding-tf-idf/. Accessed 13 Sep, 2022

  47. Tan WK, Hassanpour S, Heagerty PJ, Rundell SD, Suri P, Huhdanpaa HT et al (2018) Comparison of natural language processing rules-based and machine-learning systems to identify lumbar spine imaging findings related to low back pain. Acad Radiol 25:1422–1432

    Article  PubMed  PubMed Central  Google Scholar 

  48. Tavabi N, Singh M, Pruneski J, Kiapour AM (2022) Systematic evaluation of common natural language processing techniques to codify clinical notes. medRxiv. https://doi.org/10.1101/2022.10.10.222808522022

    Article  Google Scholar 

  49. Thirukumaran CP, Zaman A, Rubery PT, Calabria C, Li Y, Ricciardi BF et al (2019) Natural language processing for the identification of surgical site infections in orthopedics. J Bone Joint Surg Am 101:2167–2174

    Article  PubMed  Google Scholar 

  50. Tibbo ME, Wyles CC, Fu S, Sohn S, Lewallen DG, Berry DJ et al (2019) Use of natural language processing tools to identify and classify periprosthetic femur fractures. J Arthroplasty 34:2216–2219

    Article  PubMed  PubMed Central  Google Scholar 

  51. Turing AM (2009) Computing machinery and intelligence. Parsing the turing test Springer Science + Media LLC. Springer, New York

    Google Scholar 

  52. VanderPlas J (2016) Python data science handbook: Essential tools for working with data. O’Reilly Media, Inc., Sebastopol

    Google Scholar 

  53. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. (2017) Attention is all you need. Paper presented at: 2017 Conference on Neural Information Processing Systems, Long Beach, 4–9 December 2017

  54. Wen A, Fu S, Moon S, El Wazir M, Rosenbaum A, Kaggal VC et al (2019) Desiderata for delivering NLP to accelerate healthcare AI advancement and a Mayo Clinic NLP-as-a-service implementation. NPJ Digit Med 2:130

    Article  PubMed  PubMed Central  Google Scholar 

  55. Wyles CC, Tibbo ME, Fu S, Wang Y, Sohn S, Kremers WK et al (2019) Use of natural language processing algorithms to identify common data elements in operative notes for total hip arthroplasty. J Bone Joint Surg Am 101:1931–1938

    Article  PubMed  Google Scholar 

Download references

Funding

No funding was necessary for this study.

Author information

Authors and Affiliations

  1. Department of Orthopedic Surgery, Boston Children’s Hospital, Boston, MA, USA

    James A. Pruneski & Ata M. Kiapour

  2. Sports Medicine and Shoulder Service, Hospital for Special Surgery, 535 East 70th Street, New York, NY, USA

    Ayoosh Pareek, Benedict U. Nwachukwu, Bryan T. Kelly, Andrew D. Pearle & Riley J. Williams III

  3. Department of Orthopedic Surgery, University of Minnesota, Minneapolis, MN, USA

    R. Kyle Martin

  4. Department of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden

    Ayoosh Pareek & Jón Karlsson

Authors
  1. James A. Pruneski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayoosh Pareek
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Benedict U. Nwachukwu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. Kyle Martin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bryan T. Kelly
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jón Karlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andrew D. Pearle
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ata M. Kiapour
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Riley J. Williams III
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ayoosh Pareek.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest for this study.

Ethical approval

Approval from the institutional review board was not necessary.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pruneski, J.A., Pareek, A., Nwachukwu, B.U. et al. Natural language processing: using artificial intelligence to understand human language in orthopedics. Knee Surg Sports Traumatol Arthrosc 31, 1203–1211 (2023). https://doi.org/10.1007/s00167-022-07272-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00167-022-07272-0

keywords

Search

Navigation