Evidence map›Paper›PMID 35913525›Full record

ArticleNeuroradiology2022

Rule-based natural language processing for automation of stroke data extraction: a validation study.

Dane Gunter, Paulo Puac-Polanco, Olivier Miguel, Rebecca E Thornhill, Amy Y X Yu, Zhongyu A Liu, Muhammad Mamdani, Chloe Pou-Prom, Richard I Aviv

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Article in Neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 17 citations in OpenAlex.

  1. Review
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  6. Machine Intelligence in Cerebrovascular and Endovascular Neurosurgery.Advances in experimental medicine and biology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

Dane GunterThe Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Paulo Puac-PolancoDepartment of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, The Ottawa Hospital Civic Campus Room C110, 1053 Carling Ave, Ottawa, ON, ON K1Y 4E9, Canada.
Olivier MiguelDepartment of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, The Ottawa Hospital Civic Campus Room C110, 1053 Carling Ave, Ottawa, ON, ON K1Y 4E9, Canada.
Rebecca E ThornhillDivision of Medical Physics, Department of Radiology, Radiation Oncology and Medical Physics, University of Ottawa, Ottawa, ON, Canada.
Amy Y X YuDepartment of Medicine (Neurology), University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Zhongyu A LiuDepartment of Medicine (Neurology), University of Toronto, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
Muhammad MamdaniDepartment of Medicine, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.
Chloe Pou-PromUnity Health Toronto, Toronto, ON, Canada.
Richard I AvivThe Ottawa Hospital Research Institute, Ottawa, ON, Canada. raviv@toh.ca.
University of Ottawa · CAUniversity of Toronto · CAHealth Sciences Centre · CAOttawa Hospital · CAUnity Health Toronto

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeData extraction from radiology free-text reports is time consuming when performed manually. Recently, more automated extraction methods using natural language processing (NLP) are proposed. A previously developed rule-based NLP algorithm showed promise in its ability to extract stroke-related data from radiology reports. We aimed to externally validate the accuracy of CHARTextract, a rule-based NLP algorithm, to extract stroke-related data from free-text radiology reports.

methodsFree-text reports of CT angiography (CTA) and perfusion (CTP) studies of consecutive patients with acute ischemic stroke admitted to a regional stroke center for endovascular thrombectomy were analyzed from January 2015 to 2021. Stroke-related variables were manually extracted as reference standard from clinical reports, including proximal and distal anterior circulation occlusion, posterior circulation occlusion, presence of ischemia or hemorrhage, Alberta stroke program early CT score (ASPECTS), and collateral status. These variables were simultaneously extracted using a rule-based NLP algorithm. The NLP algorithm's accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were assessed.

resultsThe NLP algorithm's accuracy was > 90% for identifying distal anterior occlusion, posterior circulation occlusion, hemorrhage, and ASPECTS. Accuracy was 85%, 74%, and 79% for proximal anterior circulation occlusion, presence of ischemia, and collateral status respectively. The algorithm confirmed the absence of variables from radiology reports with an 87-100% accuracy.

conclusionsRule-based NLP has a moderate to good performance for stroke-related data extraction from free-text imaging reports. The algorithm's accuracy was affected by inconsistent report styles and lexicon among reporting radiologists.

Indexed as

Ischemic StrokeStrokeAlgorithmsAutomationHumansNatural Language ProcessingData extractionNatural language processingRule-basedStrokeStroke surveillance

Identifiers

PMID35913525
OpenAlexW4289130391

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Registered trials

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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.