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Found 8 papers, 0 papers with code
A Hybrid Deep Learning Approach for Spatial Trigger Extraction from Radiology Reports
Radiology reports contain important clinical information about patients which are often tied through spatial expressions.
A Cross-document Coreference Dataset for Longitudinal Tracking across Radiology Reports
This is one of the first attempts focusing on CDCR in the clinical domain and holds potential in benefiting physicians and clinical research through long-term tracking of radiology findings.
coreference-resolution
Cross Document Coreference Resolution
Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes
This is the first work to represent and extract a wide variety of clinical information in ophthalmology.
Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping
Classifying fine-grained ischemic stroke phenotypes relies on identifying important clinical information.
RadLex Normalization in Radiology Reports
Further, no study to date has attempted to leverage RadLex for standardization.
Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports
The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more accurate representations of spatial language used by radiologists.
Understanding Spatial Language in Radiology: Representation Framework, Annotation, and Spatial Relation Extraction from Chest X-ray Reports using Deep Learning
We define a representation framework for extracting spatial information from radiology reports (Rad-SpRL).
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes
Conclusion: The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes.
