ArticleBMJ open2020
Generating high-quality data abstractions from scanned clinical records: text-mining-assisted extraction of endometrial carcinoma pathology features as proof of principle.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Epidemiological Assessment of Oral and Oropharyngeal Squamous Cell Carcinomas Before, During, and After the COVID-19 Pandemic, 2018-2024.Journal of clinical medicine · 2026Article
- TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models.Patterns (New York, N.Y.) · 2024Article
- Deep learning-based NLP data pipeline for EHR-scanned document information extraction.JAMIA open · 2022Article
- A Smartphone App to Increase Immunizations in the Pediatric Solid Organ Transplant Population: Development and Initial Usability Study.JMIR formative research · 2022Article
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Authors and funding
6 authors.
Funding
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Abstract
objectiveMedical research studies often rely on the manual collection of data from scanned typewritten clinical records, which can be laborious, time consuming and error prone because of the need to review individual clinical records. We aimed to use text mining to assist with the extraction of clinical features from complex text-based scanned pathology records for medical research studies.
designText mining performance was measured by extracting and annotating three distinct pathological features from scanned photocopies of endometrial carcinoma clinical pathology reports, and comparing results to manually abstracted terms. Inclusion and exclusion keyword trigger terms to capture leiomyomas, endometriosis and adenomyosis were provided based on expert knowledge. Terms were expanded with character variations based on common optical character recognition (OCR) error patterns as well as negation phrases found in sample reports. The approach was evaluated on an unseen test set of 1293 scanned pathology reports originating from laboratories across Australia.
settingScanned typewritten pathology reports for women aged 18-79 years with newly diagnosed endometrial cancer (2005-2007) in Australia.
resultsHigh concordance with final abstracted codes was observed for identifying the presence of three pathology features (94%-98% F-measure). The approach was more consistent and reliable than manual abstractions, identifying 3%-14% additional feature instances.
conclusionKeyword trigger-based automation with OCR error correction and negation handling proved not only to be rapid and convenient, but also providing consistent and reliable data abstractions from scanned clinical records. In conjunction with manual review, it can assist in the generation of high-quality data abstractions for medical research studies.
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