TY - JOUR
T1 - Application of Natural Language Processing in Total Joint Arthroplasty
T2 - Opportunities and Challenges
AU - Nugen, Fred
AU - Vera Garcia, Diana V.
AU - Sohn, Sunghwan
AU - Mickley, John P.
AU - Wyles, Cody C.
AU - Erickson, Bradley J.
AU - Taunton, Michael J.
N1 - Publisher Copyright:
© 2023 Elsevier Inc.
PY - 2023/10
Y1 - 2023/10
N2 - Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electronic health records only accessible by manual extraction, which takes extensive time and resources. Natural language processing (NLP), a field within artificial intelligence, may offer a viable alternative to manual extraction. Using NLP, a researcher can analyze written and spoken data and extract data in an organized manner suitable for future research and clinical use. This article will first discuss common subtasks involved in an NLP pipeline, including data preparation, modeling, analysis, and external validation, followed by examples of NLP projects. Challenges and limitations of NLP will be discussed, closing with future directions of NLP projects, including large language models.
AB - Total joint arthroplasty is becoming one of the most common surgeries within the United States, creating an abundance of analyzable data to improve patient experience and outcomes. Unfortunately, a large majority of this data is concealed in electronic health records only accessible by manual extraction, which takes extensive time and resources. Natural language processing (NLP), a field within artificial intelligence, may offer a viable alternative to manual extraction. Using NLP, a researcher can analyze written and spoken data and extract data in an organized manner suitable for future research and clinical use. This article will first discuss common subtasks involved in an NLP pipeline, including data preparation, modeling, analysis, and external validation, followed by examples of NLP projects. Challenges and limitations of NLP will be discussed, closing with future directions of NLP projects, including large language models.
KW - arthroplasty
KW - machine learning
KW - natural language processing
KW - orthopedic surgery
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U2 - 10.1016/j.arth.2023.08.047
DO - 10.1016/j.arth.2023.08.047
M3 - Article
C2 - 37619802
AN - SCOPUS:85170087260
SN - 0883-5403
VL - 38
SP - 1948
EP - 1953
JO - Journal of Arthroplasty
JF - Journal of Arthroplasty
IS - 10
ER -