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

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
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
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.
In: Technical innovations & patient support in radiation oncology. - : Elsevier BV. - 2405-6324. ; 25
  • Journal article (peer-reviewed)
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)

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