ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Automated identification of incidentalomas requiring follow-up: A multi-anatomy evaluation of LLM-based and supervised approaches.Journal of biomedical informatics · 2026Article
- Automatic Abstraction of Computed Tomography Imaging Indication Using Natural Language Processing for Evaluation of Surveillance Patterns in Long-Term Lung Cancer Survivors.JCO clinical cancer informatics · 2025Article
- In-Context Learning with Large Language Models: A Simple and Effective Approach to Improve Radiology Report Labeling.Healthcare informatics research · 2025Article
- Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.AJR. American journal of roentgenology · 2025Article
- A scoping review of large language model based approaches for information extraction from radiology reports.NPJ digital medicine · 2024Article
- Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.AJR. American journal of roentgenology · 2023Article
- Health Disparities in Lapses in Diabetic Retinopathy Care.Ophthalmology science · 2023Article
- Event-Based Clinical Finding Extraction from Radiology Reports with Pre-trained Language Model.Journal of digital imaging · 2023Article
- Analysis of Radiology Report Recommendation Characteristics and Rate of Recommended Action Performance.JAMA network open · 2022Article
- Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. To train our extraction models, we created a corpus of 1367 radiology reports annotated for recommendation information. Our extraction models achieved 0.93 f-score for recommendation sentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score for time frame. We applied the extraction models to a set of over 3.3 million radiology reports and analyzed the adherence of follow-up recommendations.
Identifiers
32477653PMC7233090What 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.