ArticleUpsala journal of medical sciences2022
Automated data extraction of electronic medical records: Validity of data mining to construct research databases for eligibility in gastroenterological clinical trials.
Article in Upsala journal of medical sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- TheraMind: a multi-LLM ensemble for accelerating drug repurposing in lung cancer via case report mining.NPJ precision oncology · 2026Article
- Bridging the gap between models and reality: development of a research environment for an object-oriented hospital information system to integrate artificial intelligence and robotics into clinical practice.International journal of computer assisted radiology and surgery · 2025Article
- Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation.European journal of radiology open · 2024Article
- Urban-Rural Disparities in Community Participation after Spinal Cord Injury in Ontario.Healthcare (Basel, Switzerland) · 2024Article
- Behind the scenes: Key lessons learned from the RELIEVE-AKI clinical trial.Journal of critical care · 2024Review
- A pragmatic methodology to extract anesthetic and physiological data from the electronic health record.Paediatric anaesthesia · 2024Article
- Ulcerative colitis progression: a retrospective analysis of disease burden using electronic medical records.Upsala journal of medical sciences · 2022Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic medical records (EMRs) are adopted for storing patient-related healthcare information. Using data mining techniques, it is possible to make use of and derive benefit from this massive amount of data effectively. We aimed to evaluate validity of data extracted by the Customized eXtraction Program (CXP).
methodsThe CXP extracts and structures data in rapid standardised processes. The CXP was programmed to extract TNFα-native active ulcerative colitis (UC) patients from EMRs using defined International Classification of Disease-10 (ICD-10) codes. Extracted data were read in parallel with manual assessment of the EMR to compare with CXP-extracted data.
resultsFrom the complete EMR set, 2,802 patients with code K51 (UC) were extracted. Then, CXP extracted 332 patients according to inclusion and exclusion criteria. Of these, 97.5% were correctly identified, resulting in a final set of 320 cases eligible for the study. When comparing CXP-extracted data against manually assessed EMRs, the recovery rate was 95.6-101.1% over the years with 96.1% weighted average sensitivity.
conclusionUtilisation of the CXP software can be considered as an effective way to extract relevant EMR data without significant errors. Hence, by extracting from EMRs, CXP accurately identifies patients and has the capacity to facilitate research studies and clinical trials by finding patients with the requested code as well as funnel down itemised individuals according to specified inclusion and exclusion criteria. Beyond this, medical procedures and laboratory data can rapidly be retrieved from the EMRs to create tailored databases of extracted material for immediate use in clinical trials.
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