À la Recherche du Temps Perdu: extracting temporal relations from medical text in the 2012 i2b2 NLP challenge

Autor: Colin Cherry, Berry de Bruijn, Joel Martin, Xiaodan Zhu
Jazyk: angličtina
Rok vydání: 2013
Předmět:
Patient Discharge Summaries
Post hoc
Computer science
temporal analysis
relation extraction
Information Storage and Retrieval
Health Informatics
02 engineering and technology
data extraction
computer.software_genre
medical record
Research and Applications
Task (project management)
Time
Translational Research
Biomedical
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
Hospital discharge
Electronic Health Records
Humans
030212 general & internal medicine
information extraction
natural language processing
semantics
Recall
accuracy
temporal reasoning
business.industry
medical specialist
prediction
Relationship extraction
clinical text
hospital discharge
Information extraction
machine learning
Artificial intelligence
F1 score
business
computer
Natural language processing
Zdroj: Journal of the American Medical Informatics Association : JAMIA
ISSN: 1527-974X
1067-5027
Popis: Objective: An analysis of the timing of events is critical for a deeper understanding of the course of events within a patient record. The 2012 i2b2 NLP challenge focused on the extraction of temporal relationships between concepts within textual hospital discharge summaries. Materials and methods: The team from the National Research Council Canada (NRC) submitted three system runs to the second track of the challenge: typifying the time-relationship between pre-annotated entities. The NRC system was designed around four specialist modules containing statistical machine learning classifiers. Each specialist targeted distinct sets of relationships: local relationships, 'sectime'-type relationships, non-local overlap-type relationships, and non-local causal relationships. Results: The best NRC submission achieved a precision of 0.7499, a recall of 0.6431, and an F1 score of 0.6924, resulting in a statistical tie for first place. Post hoc improvements led to a precision of 0.7537, a recall of 0.6455, and an F1 score of 0.6954, giving the highest scores reported on this task to date. Discussion and conclusions: Methods for general relation extraction extended well to temporal relations, and gave top-ranked state-of-the-art results. Careful ordering of predictions within result sets proved critical to this success.
Databáze: OpenAIRE
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