Eye-SpatialNet: Spatial Information Extraction from Ophthalmology Notes
Surabhi Datta Tasneem Kaochar Hio Cheng Lam Nelly Nwosu Luca Giancardo Alice Z. Chuang Robert M. Feldman Kirk Roberts
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.