Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes
We introduce an annotated corpus of 600 ophthalmology notes labeled with detailed spatial and contextual information of ophthalmic entities. We extend our previously proposed frame semantics-based spatial representation schema, Rad-SpatialNet, to represent spatial language in ophthalmology text, resulting in the Eye-SpatialNet schema. The spatially-grounded entities are findings, procedures, and drugs. To accurately capture all spatial details, we add some domain-specific elements in Eye-SpatialNet. The annotated corpus contains 1715 spatial triggers, 7308 findings, 2424 anatomies, and 9914 descriptors. To automatically extract the spatial information, we employ a two-turn question answering approach based on the transformer language model BERT. The results are promising, with F1 scores of 89.31, 74.86, and 88.47 for spatial triggers, Figure, and Ground frame elements, respectively. This is the first work to represent and extract a wide variety of clinical information in ophthalmology. Extracting detailed information can benefit ophthalmology applications and research targeted toward disease progression and screening.
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingQuestion AnsweringMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SpatialNet: A Declarative Resource for Spatial Relations
This paper introduces SpatialNet, a novel resource which links linguistic expressions to actual spatial configurations. SpatialNet is based on FrameNet (Ruppenhofer et al., 2016) and VigNet (Coyne et al., 2011), two reso…
Automatic Speech Recognition (ASR)Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports
This paper proposes a representation framework for encoding spatial language in radiology based on frame semantics. The framework is adopted from the existing SpatialNet representation in the general domain with the aim …
Leveraging Spatial Information in Radiology Reports for Ischemic Stroke Phenotyping
Classifying fine-grained ischemic stroke phenotypes relies on identifying important clinical information. Radiology reports provide relevant information with context to determine such phenotype information. We focus on s…
LSZone: A Lightweight Spatial Information Modeling Architecture for Real-time In-car Multi-zone Speech Separation
In-car multi-zone speech separation, which captures voices from different speech zones, plays a crucial role in human-vehicle interaction. Although previous SpatialNet has achieved notable results, its high computational…
Information ExtractionSpeech SeparationMulti-person Articulated Tracking with Spatial and Temporal Embeddings
We propose a unified framework for multi-person pose estimation and tracking. Our framework consists of two main components,~\ie~SpatialNet and TemporalNet. The SpatialNet accomplishes body part detection and part-level …
Multi-Object TrackingMulti-Person Pose EstimationMulti-Person Pose Estimation and TrackingObject Tracking+2