{"title":"A Framework for Comprehensive Electronic QA in Radiation Therapy","authors":"J. Kildea, M. Evans, W. Parker","doi":"10.1109/ICMLA.2010.157","DOIUrl":null,"url":null,"abstract":"We describe a framework for comprehensive electronic QA currently under development in the department of Radiation Oncology at the Montreal General Hospital. When complete, the system will incorporate all data generated within the department. It will allow for easy access to all aspects of a patient’s treatment and to the state of all relevant equipment at the time of treatment. Quality control will be achieved through automated Shewhart-type control charting with appropriate statistical analysis. Machine learning methods will be used to examine the data in order to search for errors, inconsistencies and unnecessary treatment delays. The system will entail a web interface internal to the clinic and written in PERL.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"183 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Ninth International Conference on Machine Learning and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2010.157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
We describe a framework for comprehensive electronic QA currently under development in the department of Radiation Oncology at the Montreal General Hospital. When complete, the system will incorporate all data generated within the department. It will allow for easy access to all aspects of a patient’s treatment and to the state of all relevant equipment at the time of treatment. Quality control will be achieved through automated Shewhart-type control charting with appropriate statistical analysis. Machine learning methods will be used to examine the data in order to search for errors, inconsistencies and unnecessary treatment delays. The system will entail a web interface internal to the clinic and written in PERL.
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