ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2013
Automatically pairing measured findings across narrative abdomen CT reports.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
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- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.Journal of digital imaging · 2019Article
- Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.JCO clinical cancer informatics · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiological measurements are one of the key variables in widely adopted guidelines (WHO, RECIST) that standardize and objectivize response assessment in oncology care. Measurements are typically described in free-text, narrative radiology reports. We present a natural language processing pipeline that extracts measurements from radiology reports and pairs them with extracted measurements from prior reports of the same clinical finding, e.g., lymph node or mass. A ground truth was created by manually pairing measurements in the abdomen CT reports of 50 patients. A Random Forest classifier trained on 15 features achieved superior results in an end-to-end evaluation of the pipeline on the extraction and pairing task: precision 0.910, recall 0.878, F-measure 0.894, AUC 0.988. Representing the narrative content in terms of UMLS concepts did not improve results. Applications of the proposed technology include data mining, advanced search and workflow support for healthcare professionals managing radiological measurements.
Indexed as
Identifiers
24551406PMC3900143W25545242What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.