Difference between revisions of "Hyeju Jang et al IRI 2006"
PastStudents (talk | contribs) |
PastStudents (talk | contribs) |
||
| Line 30: | Line 30: | ||
The approach of the system is Hidden Markov Model (HMM) using equivalence classes based on primitive tagging results such as UMLS and POS to solve serious data sparse problem. | The approach of the system is Hidden Markov Model (HMM) using equivalence classes based on primitive tagging results such as UMLS and POS to solve serious data sparse problem. | ||
| − | [[File: | + | [[File:seta.jpg]] |
The performance of the system is not high, approximately 70%. | The performance of the system is not high, approximately 70%. | ||
The author thinks this could be improved with reflecting various aspects of language. | The author thinks this could be improved with reflecting various aspects of language. | ||
Revision as of 02:40, 1 October 2010
Citation
Hyeju Jang, Yun Jin, Sung Hyon Myaeng. 2006. Integration of Low Level Linguistic Information for Clinical Document Semantic Tagging. In Proceeding of IEEE Conference on Information Reuse and Integration, 292-297.
Online version
Summary
The paper presents a semantic tagger which extract "symptom", "therapy", and "performance" from free-text clinical records in order to help answering questions below.
1. How can X be used in the treatment of Y?
2. What are the performance characteristics of X in the setting of Y?
Texts were written by Korean doctors. The characteristics of the texts are
- Specialized medical words
- Ex) hypothyroidism , hypertensive
- Abbreviations
- Ex) ACA, MRI, CR
- non-alphanumeric symbols
- Ex) , ↑
- numeric data
- Ex) 0.24/73.2 , 1-2회 , 12.1% , 02.12.16-03.1.2
- Mixed Korean and English
- English for the medical terminologies
- Korean for some general nouns and most verbs
- Ex) 시행한 basal hormone level 에서, clinical 하게는 cushinoid feature 처럼 보이나
The approach of the system is Hidden Markov Model (HMM) using equivalence classes based on primitive tagging results such as UMLS and POS to solve serious data sparse problem.
The performance of the system is not high, approximately 70%. The author thinks this could be improved with reflecting various aspects of language.
