放射治疗外部应用的自动数据提取工具(DET)

Q1 Nursing
Mruga Gurjar , Jesper Lindberg , Thomas Björk-Eriksson , Caroline Olsson
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引用次数: 0

摘要

目的肿瘤信息系统(OIS)管理放射治疗(RT)部门的信息。由于数据库结构的限制,存储的信息很少可以直接用于供应商特定的目的。我们的目标是通过创建一个自动数据提取、清理和格式化的工具,使这些数据能够在各种外部应用程序中使用。方法和材料:我们使用了瑞典9个直线加速器RT部门的OIS数据(2015–16年,70周)。提取的数据包括患者的转诊和预约,以及RT子任务的详细信息。准备数据供外部使用的数据提取工具是用C#编程语言构建的。它使用excel自动化查询来删除未分配/重复的值,替换丢失的数据,并执行特定于应用程序的计算。使用描述性统计来验证相应时间段的手动准备数据集的输出。结果:根据最初的原始数据,2030(51%)/907(23%)名患者对84种不同的癌症诊断具有已知的治疗和姑息治疗意图。在删除不完整的条目后,373名(10%)患者有未知的治疗意图,这些意图是根据已知的治疗/姑息比率进行替代的。自动和手动准备的数据集不同<;1%用于模具、治疗计划、质量保证,±5%用于分数和磁共振成像,该工具在80/140(57%)条目中高估。结论:我们成功地实现了一个软件工具,为外部应用程序准备了现成的OIS数据集。我们的评估显示,总体结果接近手动准备的数据集。使用我们的自动化策略准备数据集所需的时间可以将手动准备的时间从几周减少到几秒钟。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automated data extraction tool (DET) for external applications in radiotherapy

Automated data extraction tool (DET) for external applications in radiotherapy

Automated data extraction tool (DET) for external applications in radiotherapy

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.

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来源期刊
CiteScore
4.10
自引率
0.00%
发文量
48
审稿时长
67 days
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