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Automated data extr...
Automated data extraction tool (DET) for external applications in radiotherapy.
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- Gurjar, Mrugaja, 1997 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för medicinsk strålningsvetenskap,Institute of Clinical Sciences, Department of Medical Radiation Sciences
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- Lindberg, Jesper, 1983 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för medicinsk strålningsvetenskap,Institute of Clinical Sciences, Department of Medical Radiation Sciences
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- Björk-Eriksson, Thomas, 1960 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för onkologi,Institute of Clinical Sciences, Department of Oncology
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- Olsson, Caroline, 1970 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för medicinsk strålningsvetenskap,Institute of Clinical Sciences, Department of Medical Radiation Sciences
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(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- English.
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In: Technical innovations & patient support in radiation oncology. - : Elsevier BV. - 2405-6324. ; 25
- Related links:
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https://gup.ub.gu.se...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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.We used OIS data from a nine-linac RT department in Sweden (70weeks, 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.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.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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Publication and Content Type
- ref (subject category)
- art (subject category)
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