Abstract
This paper describes the approach of the DIT AIGroup to the i2b2 Obesity Challenge to build a system to diagnose obesity and related co-morbidities from narrative, unstructured patient records. Based on experimental results a system was developed which used knowledge-light text classification using decision trees, and negation labelling.
| Original language | English |
|---|---|
| DOIs | |
| Publication status | Published - 2008 |
| Event | Second i2b2 Shared-Task Workshop on Challenges in Natural Language Processing for Clinical Data, American Medical Informatics Association Annual Conference - Duration: 1 Jan 2008 → … |
Conference
| Conference | Second i2b2 Shared-Task Workshop on Challenges in Natural Language Processing for Clinical Data, American Medical Informatics Association Annual Conference |
|---|---|
| Period | 1/01/08 → … |
| Other | AMIA '08 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- i2b2 Obesity Challenge
- diagnose obesity
- co-morbidities
- narrative
- unstructured patient records
- knowledge-light text classification
- decision trees
- negation labelling
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