Difference between revisions of "Zhou et al ACM symposium on Applied Computing 2006"
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* Extraction of medical terms | * Extraction of medical terms | ||
* [[AddressesProblem::Relation Extraction]] | * [[AddressesProblem::Relation Extraction]] | ||
| − | + | In this paper, relation extraction means extraction of associated medical concepts. For example, 'Blood pressure' and '144/90' are associated terms in the sentence, "Blood pressure is 144/90". | |
* [[AddressesProblem::Text Classification]] | * [[AddressesProblem::Text Classification]] | ||
| − | + | For example, a patient can be classified as a former smoker, a current smoker, or a non-smoker | |
Their approaches are: | Their approaches are: | ||
* An ontology-based approach for extracting medical terms of interest | * An ontology-based approach for extracting medical terms of interest | ||
| − | + | They used Unified Medical Language System (UMLS). About terms that are not defined in UMLS, they predicted categories of some terms using sentence structures. | |
* A graph-based approach which uses the parsing result of link-grammar parser for [[AddressesProblem::Relation Extraction]] | * A graph-based approach which uses the parsing result of link-grammar parser for [[AddressesProblem::Relation Extraction]] | ||
| − | + | Notable things in their approach are three. First, they included the processing of negation. Second, when the parser fails, they used a pattern-based approach. Lastly, they replaced multi-word terms with placeholders because the parser did not process the terms. | |
* an NLP-based feature extraction method coupled with an ID3-based [[AddressesProblem::Decision Tree Learning]] for [[AddressesProblem::Text Classification]] | * an NLP-based feature extraction method coupled with an ID3-based [[AddressesProblem::Decision Tree Learning]] for [[AddressesProblem::Text Classification]] | ||
Revision as of 17:15, 9 October 2010
Citation
Ciaohua Zhou et al. 2006. Approaches to Text Mining for Clinical Medical Records. In Proceedings of the 2006 ACM symposium on Applied computing, 235-239.
Online version
Summary
The paper presents a MEDical Information Extraction (MedIE) system, which extracts patient information from free-text clinical records.
They divided their extraction job into three tasks below.
- Extraction of medical terms
- Relation Extraction
In this paper, relation extraction means extraction of associated medical concepts. For example, 'Blood pressure' and '144/90' are associated terms in the sentence, "Blood pressure is 144/90".
For example, a patient can be classified as a former smoker, a current smoker, or a non-smoker
Their approaches are:
- An ontology-based approach for extracting medical terms of interest
They used Unified Medical Language System (UMLS). About terms that are not defined in UMLS, they predicted categories of some terms using sentence structures.
- A graph-based approach which uses the parsing result of link-grammar parser for Relation Extraction
Notable things in their approach are three. First, they included the processing of negation. Second, when the parser fails, they used a pattern-based approach. Lastly, they replaced multi-word terms with placeholders because the parser did not process the terms.
- an NLP-based feature extraction method coupled with an ID3-based Decision Tree Learning for Text Classification
This approach was fairly successful mostly showing over 80% of precision and recall. However, the system was tested on the data written by only a clinician, which means that the style of free-text records was consistent. Nevertheless, the research is worth in that they applied various IE techniques to the free-text clinical records, explain about the problems they encountered.
Related papers
An interesting follow-up paper is Denecke and Bernauer AIME 2007 which uses semantic structures to extract medical information.
