ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Spatial Relation Extraction from Radiology Reports using Syntax-Aware Word Representations.
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 1 paper.
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Who cites it
1 citing paper in PubMed.
- A scoping review of large language model based approaches for information extraction from radiology reports.NPJ digital medicine · 2024Article
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Authors and funding
2 authors.
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Abstract
In this paper, we investigate the task of spatial role labeling for extracting spatial relations from chest X-ray reports. Previous works have shown the usefulness of incorporating syntactic information in extracting spatial relations. We propose syntax-enhanced word representations in addition to word and character embeddings for extracting radiologyspecific spatial roles. We utilize a bidirectional long short-term memory (Bi-LSTM) conditional random field (CRF) as the baseline model to capture the word sequence and employ additional Bi-LSTMs to encode syntax based on dependency tree substructures. Our focus is on empirically evaluating the contribution of each syntax integration method in extracting the spatial roles with respect to a SPATIAL INDICATOR in a sentence. The incorporation of syntax embeddings to the baseline method achieves promising results, with improvements of 1.3, 0.8, 4.6, and 4.6 points in the average F1 measures for TRAJECTOR, LANDMARK, DIAGNOSIS, and HEDGE roles, respectively.
Identifiers
32477630PMC7233067What 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.