Original paper
Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes
Abstract
Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient’s natural history. In this paper, we concentrate on extracting from clinical...
Paper Details
Title
Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes
Published Date
Jan 19, 2021
Journal
Volume
11
Issue
2
Pages
865 - 865
Citation AnalysisPro
You’ll need to upgrade your plan to Pro
Looking to understand the true influence of a researcher’s work across journals & affiliations?
- Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
- Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.
Notes
History