ArticleInternational journal of medical informatics2019
Identifying incidental findings from radiology reports of trauma patients: An evaluation of automated feature representation methods.
Article in International journal of medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Open LLM-based actionable incidental finding extraction from [Frontiers in digital health · 2025Article
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- Development and web deployment of an automated neuroradiology MRI protocoling tool with natural language processing.BMC medical informatics and decision making · 2021Article
- A Web Application for Adrenal Incidentaloma Identification, Tracking, and Management Using Machine Learning.Applied clinical informatics · 2020Article
- Interactive NLP in Clinical Care: Identifying Incidental Findings in Radiology Reports.Applied clinical informatics · 2019Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundRadiologic imaging of trauma patients often uncovers findings that are unrelated to the trauma. These are termed as incidental findings and identifying them in radiology examination reports is necessary for appropriate follow-up. We developed and evaluated an automated pipeline to identify incidental findings at sentence and section levels in radiology reports of trauma patients.
methodsWe created an annotated dataset of 4,181 reports and investigated automated feature representations including traditional word and clinical concept (such as SNOMED CT) representations, as well as word and concept embeddings. We evaluated these representations by using them with traditional classifiers such as logistic regression and with deep learning methods such as convolutional neural networks (CNNs).
resultsThe best performance was observed using word embeddings with CNNs with F
conclusionThe results show that the best performance was achieved by using embeddings with CNNs at both sentence and section levels. This provides evidence that such a pipeline is capable of accurately identifying incidental findings in radiology reports in an automated manner.
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