{"title":"放射治疗外部应用的自动数据提取工具(DET)","authors":"Mruga Gurjar , Jesper Lindberg , Thomas Björk-Eriksson , Caroline Olsson","doi":"10.1016/j.tipsro.2022.12.001","DOIUrl":null,"url":null,"abstract":"<div><h3>Purpose</h3><p>Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting. Methods and materials: We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015–16). Extracted data included patients’ referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period. Results: From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and ± 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool. Conclusion: We successfully implemented a software tool to prepare ready-to-use OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.</p></div>","PeriodicalId":36328,"journal":{"name":"Technical Innovations and Patient Support in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842687/pdf/","citationCount":"0","resultStr":"{\"title\":\"Automated data extraction tool (DET) for external applications in radiotherapy\",\"authors\":\"Mruga Gurjar , Jesper Lindberg , Thomas Björk-Eriksson , Caroline Olsson\",\"doi\":\"10.1016/j.tipsro.2022.12.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Purpose</h3><p>Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting. Methods and materials: We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015–16). Extracted data included patients’ referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period. Results: From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and ± 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool. Conclusion: We successfully implemented a software tool to prepare ready-to-use OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.</p></div>\",\"PeriodicalId\":36328,\"journal\":{\"name\":\"Technical Innovations and Patient Support in Radiation Oncology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842687/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Technical Innovations and Patient Support in Radiation Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S240563242200049X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Nursing\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technical Innovations and Patient Support in Radiation Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S240563242200049X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Nursing","Score":null,"Total":0}
Automated data extraction tool (DET) for external applications in radiotherapy
Purpose
Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting. Methods and materials: We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015–16). Extracted data included patients’ referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period. Results: From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and ± 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool. Conclusion: We successfully implemented a software tool to prepare ready-to-use OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.