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

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(Created page with '== Citation == Turney, P. D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of…')
 
 
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== Citation ==
 
== Citation ==
  
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Turney, P. D. 2002. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, 417–424.
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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 ==
 
== Online version ==
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== Summary ==
 
== Summary ==
  
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This is an early and influential [[Category::paper]] presenting an unsupervised approach to [[AddressesProblem::review classification]]. The basic ideas are:
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The [[Category::paper]] presents a MEDical Information Extraction (MedIE) system, which extracts patient information from free-text clinical records.  
  
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* To use patterns of part of speech tags to pick out phrases that are likely to be meaningful and unambiguous with respect to semantic orientation (e.g. ADJ NOUN might pick out "good service" or "delicious desserts").
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They divided their extraction job into three tasks below.
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* Extraction of medical terms
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* [[AddressesProblem::Relation Extraction]] 
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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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* [[AddressesProblem::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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* To use [[UsesMethod::pointwise mutual information]] (PMI) to score the similarity of each phrase in a review with the two words "excellent" or "poor", and give an overall score for the polarity to each phrase based on the difference of its PMI with "excellent" to the PMI with "poor".  A large corpus was used here (the Web, via queries to a search engine).
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Their approaches are:
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* An ontology-based approach for extracting medical terms of interest
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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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* A graph-based approach which uses the parsing result of link-grammar parser for [[AddressesProblem::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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* an NLP-based feature extraction method coupled with an ID3-based [[UsesMethod::Decision Tree Learning]] for [[AddressesProblem::Text Classification]]
  
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* To score the polarity of a review based on the total polarity of the phrases in it.
 
  
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This approach was fairly successful on a range of review-classification tasks: it achieved accuracy of between 65% and 85% in predicting an author-assigned "recommended" flag for Epinions ratings for eight diverse products, ranging from cars to movies. Many later writers used several key ideas from the paper, including: treating polarity prediction as a document-classification problem; classifying documents based on likely-to-be-informative phrases; and using unsupervised or semi-supervised learning methods.
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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 ==
 
== 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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