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Natural language processing in low back pain and spine diseases: A systematic review

Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and rese...

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Autores principales: Bacco, Luca, Russo, Fabrizio, Ambrosio, Luca, D’Antoni, Federico, Vollero, Luca, Vadalà, Gianluca, Dell’Orletta, Felice, Merone, Mario, Papalia, Rocco, Denaro, Vincenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329654/
https://www.ncbi.nlm.nih.gov/pubmed/35910476
http://dx.doi.org/10.3389/fsurg.2022.957085
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author Bacco, Luca
Russo, Fabrizio
Ambrosio, Luca
D’Antoni, Federico
Vollero, Luca
Vadalà, Gianluca
Dell’Orletta, Felice
Merone, Mario
Papalia, Rocco
Denaro, Vincenzo
author_facet Bacco, Luca
Russo, Fabrizio
Ambrosio, Luca
D’Antoni, Federico
Vollero, Luca
Vadalà, Gianluca
Dell’Orletta, Felice
Merone, Mario
Papalia, Rocco
Denaro, Vincenzo
author_sort Bacco, Luca
collection PubMed
description Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years. It motivated us to systematically review the literature comprised of two major public databases, PubMed and Scopus. To do so, we first formulated our research question following the PICO guidelines. Then, we followed a PRISMA-like protocol by performing a search query including terminologies of both technical (e.g., natural language and computational linguistics) and clinical (e.g., lumbar and spine surgery) domains. We collected 221 non-duplicated studies, 16 of which were eligible for our analysis. In this work, we present these studies divided into sub-categories, from both tasks and exploited models’ points of view. Furthermore, we report a detailed description of techniques used to extract and process textual features and the several evaluation metrics used to assess the performance of the NLP models. However, what is clear from our analysis is that additional studies on larger datasets are needed to better define the role of NLP in the care of patients with spinal disorders.
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spelling pubmed-93296542022-07-29 Natural language processing in low back pain and spine diseases: A systematic review Bacco, Luca Russo, Fabrizio Ambrosio, Luca D’Antoni, Federico Vollero, Luca Vadalà, Gianluca Dell’Orletta, Felice Merone, Mario Papalia, Rocco Denaro, Vincenzo Front Surg Surgery Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years. It motivated us to systematically review the literature comprised of two major public databases, PubMed and Scopus. To do so, we first formulated our research question following the PICO guidelines. Then, we followed a PRISMA-like protocol by performing a search query including terminologies of both technical (e.g., natural language and computational linguistics) and clinical (e.g., lumbar and spine surgery) domains. We collected 221 non-duplicated studies, 16 of which were eligible for our analysis. In this work, we present these studies divided into sub-categories, from both tasks and exploited models’ points of view. Furthermore, we report a detailed description of techniques used to extract and process textual features and the several evaluation metrics used to assess the performance of the NLP models. However, what is clear from our analysis is that additional studies on larger datasets are needed to better define the role of NLP in the care of patients with spinal disorders. Frontiers Media S.A. 2022-07-14 /pmc/articles/PMC9329654/ /pubmed/35910476 http://dx.doi.org/10.3389/fsurg.2022.957085 Text en © 2022 Bacco, Russo, Ambrosio, D'Antoni, Vollero, Vadalà, Dell'Orletta, Merone, Papalia and Denaro. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Surgery
Bacco, Luca
Russo, Fabrizio
Ambrosio, Luca
D’Antoni, Federico
Vollero, Luca
Vadalà, Gianluca
Dell’Orletta, Felice
Merone, Mario
Papalia, Rocco
Denaro, Vincenzo
Natural language processing in low back pain and spine diseases: A systematic review
title Natural language processing in low back pain and spine diseases: A systematic review
title_full Natural language processing in low back pain and spine diseases: A systematic review
title_fullStr Natural language processing in low back pain and spine diseases: A systematic review
title_full_unstemmed Natural language processing in low back pain and spine diseases: A systematic review
title_short Natural language processing in low back pain and spine diseases: A systematic review
title_sort natural language processing in low back pain and spine diseases: a systematic review
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329654/
https://www.ncbi.nlm.nih.gov/pubmed/35910476
http://dx.doi.org/10.3389/fsurg.2022.957085
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