Cargando…

Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study

BACKGROUND: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correl...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Shuai, Jabbour, Salma K, O'Reilly, Shannon E, Lu, James J, Dong, Lihua, Ding, Lijuan, Xiao, Ying, Yue, Ning, Wang, Fusheng, Zou, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5814605/
https://www.ncbi.nlm.nih.gov/pubmed/29391345
http://dx.doi.org/10.2196/medinform.8662
_version_ 1783300379732082688
author Zheng, Shuai
Jabbour, Salma K
O'Reilly, Shannon E
Lu, James J
Dong, Lihua
Ding, Lijuan
Xiao, Ying
Yue, Ning
Wang, Fusheng
Zou, Wei
author_facet Zheng, Shuai
Jabbour, Salma K
O'Reilly, Shannon E
Lu, James J
Dong, Lihua
Ding, Lijuan
Xiao, Ying
Yue, Ning
Wang, Fusheng
Zou, Wei
author_sort Zheng, Shuai
collection PubMed
description BACKGROUND: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies. OBJECTIVE: The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non–small cell lung cancer (NSCLC). METHODS: We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output. RESULTS: In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively. CONCLUSIONS: The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies.
format Online
Article
Text
id pubmed-5814605
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-58146052018-02-23 Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study Zheng, Shuai Jabbour, Salma K O'Reilly, Shannon E Lu, James J Dong, Lihua Ding, Lijuan Xiao, Ying Yue, Ning Wang, Fusheng Zou, Wei JMIR Med Inform Original Paper BACKGROUND: In outcome studies of oncology patients undergoing radiation, researchers extract valuable information from medical records generated before, during, and after radiotherapy visits, such as survival data, toxicities, and complications. Clinical studies rely heavily on these data to correlate the treatment regimen with the prognosis to develop evidence-based radiation therapy paradigms. These data are available mainly in forms of narrative texts or table formats with heterogeneous vocabularies. Manual extraction of the related information from these data can be time consuming and labor intensive, which is not ideal for large studies. OBJECTIVE: The objective of this study was to adapt the interactive information extraction platform Information and Data Extraction using Adaptive Learning (IDEAL-X) to extract treatment and prognosis data for patients with locally advanced or inoperable non–small cell lung cancer (NSCLC). METHODS: We transformed patient treatment and prognosis documents into normalized structured forms using the IDEAL-X system for easy data navigation. The adaptive learning and user-customized controlled toxicity vocabularies were applied to extract categorized treatment and prognosis data, so as to generate structured output. RESULTS: In total, we extracted data from 261 treatment and prognosis documents relating to 50 patients, with overall precision and recall more than 93% and 83%, respectively. For toxicity information extractions, which are important to study patient posttreatment side effects and quality of life, the precision and recall achieved 95.7% and 94.5% respectively. CONCLUSIONS: The IDEAL-X system is capable of extracting study data regarding NSCLC chemoradiation patients with significant accuracy and effectiveness, and therefore can be used in large-scale radiotherapy clinical data studies. JMIR Publications 2018-02-01 /pmc/articles/PMC5814605/ /pubmed/29391345 http://dx.doi.org/10.2196/medinform.8662 Text en ©Shuai Zheng, Salma K Jabbour, Shannon E O'Reilly, James J Lu, Lihua Dong, Lijuan Ding, Ying Xiao, Ning Yue, Fusheng Wang, Wei Zou. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 01.02.2018. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Zheng, Shuai
Jabbour, Salma K
O'Reilly, Shannon E
Lu, James J
Dong, Lihua
Ding, Lijuan
Xiao, Ying
Yue, Ning
Wang, Fusheng
Zou, Wei
Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study
title Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study
title_full Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study
title_fullStr Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study
title_full_unstemmed Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study
title_short Automated Information Extraction on Treatment and Prognosis for Non–Small Cell Lung Cancer Radiotherapy Patients: Clinical Study
title_sort automated information extraction on treatment and prognosis for non–small cell lung cancer radiotherapy patients: clinical study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5814605/
https://www.ncbi.nlm.nih.gov/pubmed/29391345
http://dx.doi.org/10.2196/medinform.8662
work_keys_str_mv AT zhengshuai automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT jabboursalmak automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT oreillyshannone automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT lujamesj automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT donglihua automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT dinglijuan automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT xiaoying automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT yuening automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT wangfusheng automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy
AT zouwei automatedinformationextractionontreatmentandprognosisfornonsmallcelllungcancerradiotherapypatientsclinicalstudy