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Knee Surgery, Sports Traumatology, Arthroscopy

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07-12-2022 | Artificial Intelligence | Review

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

Authors: James A. Pruneski, Ayoosh Pareek, Benedict U. Nwachukwu, R. Kyle Martin, Bryan T. Kelly, Jón Karlsson, Andrew D. Pearle, Ata M. Kiapour, Riley J. Williams III

Published in: Knee Surgery, Sports Traumatology, Arthroscopy | Issue 4/2023

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.

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

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

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

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 99:1–9CrossRefPubMed

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

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

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

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

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:e695-701CrossRefPubMed

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 307:1580–1582CrossRefPubMedPubMedCentral

12.

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

13.

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

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

15.

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

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 YorkCrossRef

18.

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

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

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 20:1602–1609CrossRefPubMed

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/s11042-022-13428-4CrossRefPubMedPubMedCentral

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 31:536–541CrossRefPubMed

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 36:1234–1240CrossRefPubMed

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 32:3786–3797CrossRefPubMed

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

30.

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

31.

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

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

35.

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

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/s00167-022-07181-2CrossRefPubMed

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/s00167-022-07155-4CrossRefPubMed

39.

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

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 4:86CrossRefPubMedPubMedCentral

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

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 36:922–926CrossRefPubMed

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 46:2894–2898CrossRefPubMedPubMedCentral

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:99–104CrossRefPubMed

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 25:1422–1432CrossRefPubMedPubMedCentral

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/10.1101/2022.10.10.222808522022CrossRef

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 101:2167–2174CrossRefPubMed

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 34:2216–2219CrossRefPubMedPubMedCentral

51.

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

52.

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

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 2:130CrossRefPubMedPubMedCentral

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 101:1931–1938CrossRefPubMed

Title: Natural language processing: using artificial intelligence to understand human language in orthopedics
Authors: James A. Pruneski
Ayoosh Pareek
Benedict U. Nwachukwu
R. Kyle Martin
Bryan T. Kelly
Jón Karlsson
Andrew D. Pearle
Ata M. Kiapour
Riley J. Williams III
Publication date: 07-12-2022
Publisher: Springer Berlin Heidelberg
Keyword: Artificial Intelligence
Published in: Knee Surgery, Sports Traumatology, Arthroscopy / Issue 4/2023
Print ISSN: 0942-2056
Electronic ISSN: 1433-7347
DOI: https://doi.org/10.1007/s00167-022-07272-0

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

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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