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]] | ||
| − | ** extraction of associated medical concepts | + | **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 [[UsesMethod::Decision Tree Learning]] for [[AddressesProblem::Text Classification]] | |
| − | |||
| − | * an NLP-based feature extraction method coupled with an ID3-based [[ | ||
Latest revision as of 03:54, 1 November 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".
- Text Classification
- 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.
