Loading…

Lung Cancer Diagnosis Extraction from Clinical Notes Written in Spanish

The wide adoption of electronic health records (EHRs) offers a potential source to support research. Lung cancer is one of the most common cancer in the world. Although several tools have been developed to automatically extract concepts from oncology clinical notes, still there is a gap between conc...

Full description

Saved in:
Bibliographic Details
Main Authors: Solarte-Pabon, Oswaldo, Torrente, Maria, Rodriguez-Gonzalez, Alejandro, Provencio, Mariano, Menasalvas, Ernestina, Tunas, Juan Manuel
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 497
container_issue
container_start_page 492
container_title
container_volume
creator Solarte-Pabon, Oswaldo
Torrente, Maria
Rodriguez-Gonzalez, Alejandro
Provencio, Mariano
Menasalvas, Ernestina
Tunas, Juan Manuel
description The wide adoption of electronic health records (EHRs) offers a potential source to support research. Lung cancer is one of the most common cancer in the world. Although several tools have been developed to automatically extract concepts from oncology clinical notes, still there is a gap between concept extraction and concept understanding. The high number of clinical notes for the same patient, use of negation and proper date annotations lays in the root of the problem. In this paper, we propose an approach to accurate Lung cancer diagnosis extraction from clinical notes written in Spanish. The approach deals with a disambiguation process required to extract the correct date and diagnosis of a patient having hundreds of clinical notes and consequently hundreds of annotations. Results obtained on an annotated database of 1000 patients show an F-score of 90%.
doi_str_mv 10.1109/CBMS49503.2020.00099
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9182827</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9182827</ieee_id><sourcerecordid>9182827</sourcerecordid><originalsourceid>FETCH-LOGICAL-i203t-ccdb35fc1e1f989b5cf7ee42a399bce3dd690ce0044732294a4634ceea8dd1cd3</originalsourceid><addsrcrecordid>eNotzLtOwzAUAFCDhERb-AIY_AMp168kd4TQFqQAQ0GMlWPfFKPUqewgwd8zwHS2w9i1gKUQgDfN3dNWowG1lCBhCQCIJ2wuKlkL1BLLUzaTqpIFCqzP2TznTwCjhDEztmm_4p43NjpK_D7YfRxzyHz1PSXrpjBG3qfxwJshxODswJ_HiTJ_T2GaKPIQ-fZoY8gfF-yst0Omy38X7G29em0eivZl89jctkWQoKbCOd8p0ztBoscaO-P6ikhLqxA7R8r7EsERgNaVkhK11aXSjsjW3gvn1YJd_b2BiHbHFA42_exQ1LKWlfoFTrlMzw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Lung Cancer Diagnosis Extraction from Clinical Notes Written in Spanish</title><source>IEEE Xplore All Conference Series</source><creator>Solarte-Pabon, Oswaldo ; Torrente, Maria ; Rodriguez-Gonzalez, Alejandro ; Provencio, Mariano ; Menasalvas, Ernestina ; Tunas, Juan Manuel</creator><creatorcontrib>Solarte-Pabon, Oswaldo ; Torrente, Maria ; Rodriguez-Gonzalez, Alejandro ; Provencio, Mariano ; Menasalvas, Ernestina ; Tunas, Juan Manuel</creatorcontrib><description>The wide adoption of electronic health records (EHRs) offers a potential source to support research. Lung cancer is one of the most common cancer in the world. Although several tools have been developed to automatically extract concepts from oncology clinical notes, still there is a gap between concept extraction and concept understanding. The high number of clinical notes for the same patient, use of negation and proper date annotations lays in the root of the problem. In this paper, we propose an approach to accurate Lung cancer diagnosis extraction from clinical notes written in Spanish. The approach deals with a disambiguation process required to extract the correct date and diagnosis of a patient having hundreds of clinical notes and consequently hundreds of annotations. Results obtained on an annotated database of 1000 patients show an F-score of 90%.</description><identifier>EISSN: 2372-9198</identifier><identifier>EISBN: 1728194296</identifier><identifier>EISBN: 9781728194295</identifier><identifier>DOI: 10.1109/CBMS49503.2020.00099</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cancer ; Data mining ; Diagnosis extraction ; Information Extraction ; Lung ; Lung cancer Diagnosis ; Medical diagnostic imaging ; Natural language processing ; Natural Language Processing (NLP) ; Neoplasms</subject><ispartof>2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, p.492-497</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9182827$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,778,782,787,788,23913,23914,25123,27908,54538,54915</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9182827$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Solarte-Pabon, Oswaldo</creatorcontrib><creatorcontrib>Torrente, Maria</creatorcontrib><creatorcontrib>Rodriguez-Gonzalez, Alejandro</creatorcontrib><creatorcontrib>Provencio, Mariano</creatorcontrib><creatorcontrib>Menasalvas, Ernestina</creatorcontrib><creatorcontrib>Tunas, Juan Manuel</creatorcontrib><title>Lung Cancer Diagnosis Extraction from Clinical Notes Written in Spanish</title><title>2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)</title><addtitle>CBMS</addtitle><description>The wide adoption of electronic health records (EHRs) offers a potential source to support research. Lung cancer is one of the most common cancer in the world. Although several tools have been developed to automatically extract concepts from oncology clinical notes, still there is a gap between concept extraction and concept understanding. The high number of clinical notes for the same patient, use of negation and proper date annotations lays in the root of the problem. In this paper, we propose an approach to accurate Lung cancer diagnosis extraction from clinical notes written in Spanish. The approach deals with a disambiguation process required to extract the correct date and diagnosis of a patient having hundreds of clinical notes and consequently hundreds of annotations. Results obtained on an annotated database of 1000 patients show an F-score of 90%.</description><subject>Cancer</subject><subject>Data mining</subject><subject>Diagnosis extraction</subject><subject>Information Extraction</subject><subject>Lung</subject><subject>Lung cancer Diagnosis</subject><subject>Medical diagnostic imaging</subject><subject>Natural language processing</subject><subject>Natural Language Processing (NLP)</subject><subject>Neoplasms</subject><issn>2372-9198</issn><isbn>1728194296</isbn><isbn>9781728194295</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotzLtOwzAUAFCDhERb-AIY_AMp168kd4TQFqQAQ0GMlWPfFKPUqewgwd8zwHS2w9i1gKUQgDfN3dNWowG1lCBhCQCIJ2wuKlkL1BLLUzaTqpIFCqzP2TznTwCjhDEztmm_4p43NjpK_D7YfRxzyHz1PSXrpjBG3qfxwJshxODswJ_HiTJ_T2GaKPIQ-fZoY8gfF-yst0Omy38X7G29em0eivZl89jctkWQoKbCOd8p0ztBoscaO-P6ikhLqxA7R8r7EsERgNaVkhK11aXSjsjW3gvn1YJd_b2BiHbHFA42_exQ1LKWlfoFTrlMzw</recordid><startdate>202007</startdate><enddate>202007</enddate><creator>Solarte-Pabon, Oswaldo</creator><creator>Torrente, Maria</creator><creator>Rodriguez-Gonzalez, Alejandro</creator><creator>Provencio, Mariano</creator><creator>Menasalvas, Ernestina</creator><creator>Tunas, Juan Manuel</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>202007</creationdate><title>Lung Cancer Diagnosis Extraction from Clinical Notes Written in Spanish</title><author>Solarte-Pabon, Oswaldo ; Torrente, Maria ; Rodriguez-Gonzalez, Alejandro ; Provencio, Mariano ; Menasalvas, Ernestina ; Tunas, Juan Manuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-ccdb35fc1e1f989b5cf7ee42a399bce3dd690ce0044732294a4634ceea8dd1cd3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Cancer</topic><topic>Data mining</topic><topic>Diagnosis extraction</topic><topic>Information Extraction</topic><topic>Lung</topic><topic>Lung cancer Diagnosis</topic><topic>Medical diagnostic imaging</topic><topic>Natural language processing</topic><topic>Natural Language Processing (NLP)</topic><topic>Neoplasms</topic><toplevel>online_resources</toplevel><creatorcontrib>Solarte-Pabon, Oswaldo</creatorcontrib><creatorcontrib>Torrente, Maria</creatorcontrib><creatorcontrib>Rodriguez-Gonzalez, Alejandro</creatorcontrib><creatorcontrib>Provencio, Mariano</creatorcontrib><creatorcontrib>Menasalvas, Ernestina</creatorcontrib><creatorcontrib>Tunas, Juan Manuel</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Solarte-Pabon, Oswaldo</au><au>Torrente, Maria</au><au>Rodriguez-Gonzalez, Alejandro</au><au>Provencio, Mariano</au><au>Menasalvas, Ernestina</au><au>Tunas, Juan Manuel</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Lung Cancer Diagnosis Extraction from Clinical Notes Written in Spanish</atitle><btitle>2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)</btitle><stitle>CBMS</stitle><date>2020-07</date><risdate>2020</risdate><spage>492</spage><epage>497</epage><pages>492-497</pages><eissn>2372-9198</eissn><eisbn>1728194296</eisbn><eisbn>9781728194295</eisbn><coden>IEEPAD</coden><abstract>The wide adoption of electronic health records (EHRs) offers a potential source to support research. Lung cancer is one of the most common cancer in the world. Although several tools have been developed to automatically extract concepts from oncology clinical notes, still there is a gap between concept extraction and concept understanding. The high number of clinical notes for the same patient, use of negation and proper date annotations lays in the root of the problem. In this paper, we propose an approach to accurate Lung cancer diagnosis extraction from clinical notes written in Spanish. The approach deals with a disambiguation process required to extract the correct date and diagnosis of a patient having hundreds of clinical notes and consequently hundreds of annotations. Results obtained on an annotated database of 1000 patients show an F-score of 90%.</abstract><pub>IEEE</pub><doi>10.1109/CBMS49503.2020.00099</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2372-9198
ispartof 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, p.492-497
issn 2372-9198
language eng
recordid cdi_ieee_primary_9182827
source IEEE Xplore All Conference Series
subjects Cancer
Data mining
Diagnosis extraction
Information Extraction
Lung
Lung cancer Diagnosis
Medical diagnostic imaging
Natural language processing
Natural Language Processing (NLP)
Neoplasms
title Lung Cancer Diagnosis Extraction from Clinical Notes Written in Spanish
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T02%3A03%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Lung%20Cancer%20Diagnosis%20Extraction%20from%20Clinical%20Notes%20Written%20in%20Spanish&rft.btitle=2020%20IEEE%2033rd%20International%20Symposium%20on%20Computer-Based%20Medical%20Systems%20(CBMS)&rft.au=Solarte-Pabon,%20Oswaldo&rft.date=2020-07&rft.spage=492&rft.epage=497&rft.pages=492-497&rft.eissn=2372-9198&rft.coden=IEEPAD&rft_id=info:doi/10.1109/CBMS49503.2020.00099&rft.eisbn=1728194296&rft.eisbn_list=9781728194295&rft_dat=%3Cieee_CHZPO%3E9182827%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i203t-ccdb35fc1e1f989b5cf7ee42a399bce3dd690ce0044732294a4634ceea8dd1cd3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9182827&rfr_iscdi=true