Difference between revisions of "Zhou et al ACM symposium on Applied Computing 2006"

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== Summary ==
 
== Summary ==
  
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The paper presents a MEDical Information Extraction (MedIE) system, which extracts patient information from free-text clinical records.  
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The [[Category::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.
 
They divided their extraction job into three tasks below.
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* extraction of medical terms
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* Extraction of medical terms
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* relation extraction
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* [[AddressesProblem::Relation Extraction]] 
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** extraction of associated medical concepts
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**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".
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** e.g. Blood pressure & 144/90 in the sentence, "Blood pressure is 144/90"
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* [[AddressesProblem::Text Classification]]
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* text classification
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**For example, a patient can be classified as a former smoker, a current smoker, or a non-smoker
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** e.g. 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
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** they used Unified Medical Language System (UMLS)
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**They used Unified Medical Language System (UMLS). About terms that are not defined in UMLS, they predicted categories of some terms using sentence structures.
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** About terms that are not defined in UMLS, they predicted categories of some terms using sentence structures.
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* A graph-based approach which uses the parsing result of link-grammar parser for [[AddressesProblem::Relation Extraction]]
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* A graph-based approach which uses the parsing result of link-grammar parser for relation-extraction
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**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.  
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** They included the processing of negation.
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* an NLP-based feature extraction method coupled with an ID3-based [[UsesMethod::Decision Tree Learning]] for [[AddressesProblem::Text Classification]]
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** When the parser fails, they used a pattern-based approach.
 
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** Because the parser did not process multi-word terms, they replaced the terms with placeholders.
 
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* an NLP-based feature extraction method coupled with an ID3-based decision tree for text classification
 
  
  
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== Related papers ==
 
== Related papers ==
  
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The widely cited [[RelatedPaper::Pang et al EMNLP 2002]] paper was influenced by this paper - but considers supervised learning techniques.  The choice of movie reviews as the domain was suggested by the (relatively) poor performance of Turney's method on movies.
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An interesting follow-up paper is [[RelatedPaper::Denecke and Bernauer AIME 2007]] which uses semantic structures to extract medical information.
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An interesting follow-up paper is [[RelatedPaper::Turney and Littman, TOIS 2003]] which focuses on evaluation of the technique of using PMI for predicting the [[semantic orientation of words]].
 

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

ACM portal

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.

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