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Found 6 papers, 0 papers with code
Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span mismatch.
Global Public Health Surveillance using Media Reports: Redesigning GPHIN
Global public health surveillance relies on reporting structures and transmission of trustworthy health reports.
Recognizing UMLS Semantic Types with Deep Learning
While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0. 90), our results on MedMentions are significantly lower (F1 = 0. 63), suggesting there is still plenty of opportunity for improvement on this new data.
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models
While producing a state-of-the-art result for the i2b2 2010 task (F1 = 0. 90), our results on MedMentions are significantly lower (F1 = 0. 63), suggesting there is still plenty of opportunity for improvement on this new data.
NRC-Canada at SMM4H Shared Task: Classifying Tweets Mentioning Adverse Drug Reactions and Medication Intake
Our team, NRC-Canada, participated in two shared tasks at the AMIA-2017 Workshop on Social Media Mining for Health Applications (SMM4H): Task 1 - classification of tweets mentioning adverse drug reactions, and Task 2 - classification of tweets describing personal medication intake.
Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010
Objective: As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality.
Ranked #5 on
Clinical Concept Extraction
on 2010 i2b2/VA
