A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature
The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/4/139 |
| _version_ | 1797540178958483456 |
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| author | Tanmay Basu Simon Goldsworthy Georgios V. Gkoutos |
| author_facet | Tanmay Basu Simon Goldsworthy Georgios V. Gkoutos |
| author_sort | Tanmay Basu |
| collection | DOAJ |
| description | The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors. |
| first_indexed | 2024-03-10T12:57:19Z |
| format | Article |
| id | doaj.art-af1ad90e77394322a28568fcabb4e2c5 |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T12:57:19Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-af1ad90e77394322a28568fcabb4e2c52023-11-21T11:50:05ZengMDPI AGInformation2078-24892021-03-0112413910.3390/info12040139A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant LiteratureTanmay Basu0Simon Goldsworthy1Georgios V. Gkoutos2Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UKDepartment of Radiotherapy, Somerset NHS Foundation Trust, Somerset TA1 5DA, UKInstitute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UKThe objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.https://www.mdpi.com/2078-2489/12/4/139information extractionhealth informaticsNLPtext miningmachine learningradiotherapy |
| spellingShingle | Tanmay Basu Simon Goldsworthy Georgios V. Gkoutos A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature Information information extraction health informatics NLP text mining machine learning radiotherapy |
| title | A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature |
| title_full | A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature |
| title_fullStr | A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature |
| title_full_unstemmed | A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature |
| title_short | A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature |
| title_sort | sentence classification framework to identify geometric errors in radiation therapy from relevant literature |
| topic | information extraction health informatics NLP text mining machine learning radiotherapy |
| url | https://www.mdpi.com/2078-2489/12/4/139 |
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