Difference between revisions of "Denecke and Bernauer ARTIFICIAL INTELLIGENCE IN MEDICINE 2007"
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== Summary == | == Summary == | ||
| − | The [[Category::paper]] presents a | + | The [[Category::paper]] presents a medical [[AddressesProblem::Information Extraction]] system which is based on automatic generation of semantic structures for free-text. |
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| + | A conceptual graph-like representation is produced for each sentence of a text, and then these semantic structures are exploited to extract information. | ||
They divided their extraction job into three tasks below. | They divided their extraction job into three tasks below. | ||
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* 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]] | ||
| + | This appddd | ||
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. | 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. | ||
Revision as of 20:22, 10 October 2010
Citation
Kerstin Denecke and Jochen Bernauer. 2007. Extracting Specific Medical Data Using Semantic Structures. Artificial Intelligence in Medicine, LNCS 2007 Volume 4594/2007, 257-264.
Online version
Summary
The paper presents a medical Information Extraction system which is based on automatic generation of semantic structures for free-text.
A conceptual graph-like representation is produced for each sentence of a text, and then these semantic structures are exploited to extract information.
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 appddd
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.
