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Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing
Routine cervical cancer screening has significantly decreased the incidence and mortality of cervical cancer. As selection of proper screening modalities depends on well-validated clinical decision algorithms, retrospective review correlating cytology and HPV test results with cervical biopsy diagno...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577054/ https://www.ncbi.nlm.nih.gov/pubmed/36268101 http://dx.doi.org/10.1016/j.jpi.2022.100123 |
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author | Hsu, Jim Wei-Chun Christensen, Paul Ge, Yimin Long, S. Wesley |
author_facet | Hsu, Jim Wei-Chun Christensen, Paul Ge, Yimin Long, S. Wesley |
author_sort | Hsu, Jim Wei-Chun |
collection | PubMed |
description | Routine cervical cancer screening has significantly decreased the incidence and mortality of cervical cancer. As selection of proper screening modalities depends on well-validated clinical decision algorithms, retrospective review correlating cytology and HPV test results with cervical biopsy diagnosis is essential for validating and revising these algorithms to changing technologies, demographics, and optimal clinical practices. However, manual categorization of the free-text biopsy diagnosis into discrete categories is extremely laborious due to the overwhelming number of specimens, which may lead to significant error and bias. Advances in machine learning and natural language processing (NLP), particularly over the last decade, have led to significant accomplishments and impressive performance in computer-based classification tasks. In this work, we apply an efficient version of an NLP framework, FastText™, to an annotated cervical biopsy dataset to create a supervised classifier that can assign accurate biopsy categories to free-text biopsy interpretations with high concordance to manually annotated data (>99.6%). We present cases where the machine-learning classifier disagrees with previous annotations and examine these discrepant cases after referee review by an expert pathologist. We also show that the classifier is robust on an untrained external dataset, achieving a concordance of 97.7%. In conclusion, we demonstrate a useful application of NLP to a real-world pathology classification task and highlight the benefits and limitations of this approach. |
format | Online Article Text |
id | pubmed-9577054 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-95770542022-10-19 Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing Hsu, Jim Wei-Chun Christensen, Paul Ge, Yimin Long, S. Wesley J Pathol Inform Original Research Article Routine cervical cancer screening has significantly decreased the incidence and mortality of cervical cancer. As selection of proper screening modalities depends on well-validated clinical decision algorithms, retrospective review correlating cytology and HPV test results with cervical biopsy diagnosis is essential for validating and revising these algorithms to changing technologies, demographics, and optimal clinical practices. However, manual categorization of the free-text biopsy diagnosis into discrete categories is extremely laborious due to the overwhelming number of specimens, which may lead to significant error and bias. Advances in machine learning and natural language processing (NLP), particularly over the last decade, have led to significant accomplishments and impressive performance in computer-based classification tasks. In this work, we apply an efficient version of an NLP framework, FastText™, to an annotated cervical biopsy dataset to create a supervised classifier that can assign accurate biopsy categories to free-text biopsy interpretations with high concordance to manually annotated data (>99.6%). We present cases where the machine-learning classifier disagrees with previous annotations and examine these discrepant cases after referee review by an expert pathologist. We also show that the classifier is robust on an untrained external dataset, achieving a concordance of 97.7%. In conclusion, we demonstrate a useful application of NLP to a real-world pathology classification task and highlight the benefits and limitations of this approach. Elsevier 2022-07-01 /pmc/articles/PMC9577054/ /pubmed/36268101 http://dx.doi.org/10.1016/j.jpi.2022.100123 Text en © 2022 The Authors. Published by Elsevier Inc. on behalf of Association for Pathology Informatics. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Original Research Article Hsu, Jim Wei-Chun Christensen, Paul Ge, Yimin Long, S. Wesley Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing |
title | Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing |
title_full | Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing |
title_fullStr | Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing |
title_full_unstemmed | Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing |
title_short | Classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing |
title_sort | classification of cervical biopsy free-text diagnoses through linear-classifier based natural language processing |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9577054/ https://www.ncbi.nlm.nih.gov/pubmed/36268101 http://dx.doi.org/10.1016/j.jpi.2022.100123 |
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