Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach.
Pathology reports represent a primary source of information for cancer registries. Hospitals routinely process high volumes of free-text reports, a valuable source of information regarding cancer diagnosis for improving clinical care and supporting research. Information extraction and coding of text...
| Publicado en: | Journal of Biomedical Informatics Vol. 116 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Apr2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149784909&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149784909 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Apr2021 vid: 116 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 149784909 149784909 NLM33609761 149784909 10.1016/j.jbi.2021.103712 NLM33609761 149784909 ppct: 1 formats: tig: atl: Automated classification of cancer morphology from Italian pathology reports using Natural Language Processing techniques: A rule-based approach. aug: au: Hammami, Linda Paglialonga, Alessia Pruneri, Giancarlo Torresani, Michele Sant, Milena Bono, Carlo Caiani, Enrico Gianluca Baili, Paolo affil: Analytical Epidemiology and Health Impact Unit, Fondazione IRCCS "Istituto Nazionale dei Tumori", Milan, Italy sug: subj: Neoplasms Diagnosis Natural Language Processing Language Italy Human Information Retrieval Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Questionnaires ab: Pathology reports represent a primary source of information for cancer registries. Hospitals routinely process high volumes of free-text reports, a valuable source of information regarding cancer diagnosis for improving clinical care and supporting research. Information extraction and coding of textual unstructured data is typically a manual, labour-intensive process. There is a need to develop automated approaches to extract meaningful information from such texts in a reliable and accurate way. In this scenario, Natural Language Processing (NLP) algorithms offer a unique opportunity to automatically encode the unstructured reports into structured data, thus representing a potential powerful alternative to expensive manual processing. However, notwithstanding the increasing interest in this area, there is still limited availability of NLP approaches for pathology reports in languages other than English, including Italian, to date. The aim of our work was to develop an automated algorithm based on NLP techniques, able to identify and classify the morphological content of pathology reports in the Italian language with micro-averaged performance scores higher than 95%. Specifically, a novel, domain-specific classifier that uses linguistic rules was developed and tested on 27,239 pathology reports from a single Italian oncological centre, following the International Classification of Diseases for Oncology morphology classification standard (ICD-O-M). The proposed classification algorithm achieved successful results with a micro-F1 score of 98.14% on 9594 pathology reports in the test dataset. This algorithm relies on rules defined on data from a single hospital that is specifically dedicated to cancer, but it is based on general processing steps which can be applied to different datasets. Further research will be important to demonstrate the generalizability of the proposed approach on a larger corpus from different hospitals. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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