Automated extraction of information from free text of Spanish oncology pathology reports
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| Format: | Artículo científico |
| Language: | en |
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Universidad del Valle
2023
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| _version_ | 1876456614296289280 |
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| author | Diana Marcela Mendoza-Urbano |
| author_facet | Diana Marcela Mendoza-Urbano |
| contents | Automated extraction of information from free text of Spanish oncology pathology reports Diana Marcela Mendoza-Urbano Johan Felipe Garcia Juan Sebastian Moreno Juan Carlos Bravo-Ocaña Alvaro José Riascos Angela Zambrano Harvey Sergio I Prada Medicina algorithm data science ontology learning regular expressions artificial intelligence Background: Pathology reports are stored as unstructured, ungrammatical, fragmented, and abbreviated free text with linguistic variability among pathologists. For this reason, tumor information extraction requires a significant human effort. Recording data in an efficient and high-quality format is essential in implementing and establishing a hospital-based-cancer registry Objective: This study aimed to describe implementing a natural language processing algorithm for oncology pathology reports. Methods: An algorithm was developed to process oncology pathology reports in Spanish to extract 20 medical descriptors. The approach is based on the successive coincidence of regular expressions. Results: The validation was performed with 140 pathological reports. The topography identification was performed manually by humans and the algorithm in all reports. The human identified morphology in 138 reports and by the algorithm in 137. The average fuzzy matching score was 68.3 for Topography and 89.5 for Morphology. Conclusions: A preliminary algorithm validation against human extraction was performed over a small set of reports with satisfactory results. This shows that a regular-expression approach can accurately and precisely extract multiple specimen attributes from free-text Spanish pathology reports. Additionally, we developed a website to facilitate collaborative validation at a larger scale which may be helpful for future research on the subject. 2023 artículo científico 0120-8322 https://www.redalyc.org/articulo.oa?id=28375672001 https://www.redalyc.org/journal/283/28375672001/ https://www.redalyc.org/journal/283/28375672001/html/ https://www.redalyc.org/journal/283/28375672001/28375672001.epub https://www.redalyc.org/journal/283/28375672001/movil https://doi.org/10.25100/cm.v54i1.5300 en http://www.redalyc.org/revista.oa?id=283 Colombia Médica application/pdf Universidad del Valle Colombia Médica (Colombia) Num.1 Vol.54 |
| format | Artículo científico |
| id | redalyc_28375672001 |
| institution | Redalyc |
| language | en |
| publishDate | 2023 |
| publisher | Universidad del Valle |
| spellingShingle | Automated extraction of information from free text of Spanish oncology pathology reports Diana Marcela Mendoza-Urbano Medicina algorithm data science ontology learning regular expressions artificial intelligence Automated extraction of information from free text of Spanish oncology pathology reports Diana Marcela Mendoza-Urbano Johan Felipe Garcia Juan Sebastian Moreno Juan Carlos Bravo-Ocaña Alvaro José Riascos Angela Zambrano Harvey Sergio I Prada Medicina algorithm data science ontology learning regular expressions artificial intelligence Background: Pathology reports are stored as unstructured, ungrammatical, fragmented, and abbreviated free text with linguistic variability among pathologists. For this reason, tumor information extraction requires a significant human effort. Recording data in an efficient and high-quality format is essential in implementing and establishing a hospital-based-cancer registry Objective: This study aimed to describe implementing a natural language processing algorithm for oncology pathology reports. Methods: An algorithm was developed to process oncology pathology reports in Spanish to extract 20 medical descriptors. The approach is based on the successive coincidence of regular expressions. Results: The validation was performed with 140 pathological reports. The topography identification was performed manually by humans and the algorithm in all reports. The human identified morphology in 138 reports and by the algorithm in 137. The average fuzzy matching score was 68.3 for Topography and 89.5 for Morphology. Conclusions: A preliminary algorithm validation against human extraction was performed over a small set of reports with satisfactory results. This shows that a regular-expression approach can accurately and precisely extract multiple specimen attributes from free-text Spanish pathology reports. Additionally, we developed a website to facilitate collaborative validation at a larger scale which may be helpful for future research on the subject. 2023 artículo científico 0120-8322 https://www.redalyc.org/articulo.oa?id=28375672001 https://www.redalyc.org/journal/283/28375672001/ https://www.redalyc.org/journal/283/28375672001/html/ https://www.redalyc.org/journal/283/28375672001/28375672001.epub https://www.redalyc.org/journal/283/28375672001/movil https://doi.org/10.25100/cm.v54i1.5300 en http://www.redalyc.org/revista.oa?id=283 Colombia Médica application/pdf Universidad del Valle Colombia Médica (Colombia) Num.1 Vol.54 |
| title | Automated extraction of information from free text of Spanish oncology pathology reports |
| topic | Medicina algorithm data science ontology learning regular expressions artificial intelligence |
| url | https://www.redalyc.org/articulo.oa?id=28375672001 https://www.redalyc.org/journal/283/28375672001/ https://www.redalyc.org/journal/283/28375672001/html/ https://www.redalyc.org/journal/283/28375672001/28375672001.epub https://www.redalyc.org/journal/283/28375672001/movil https://doi.org/10.25100/cm.v54i1.5300 |