Zhou et al ACM symposium on Applied Computing 2006
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 about a MEDical Information Extraction (MedIE) system, which extracts and mines patient information from free-text clinical records.
- extraction of medical terms
- text classification :e.g. a patient can be classified as a former smoker, a current smoker, or a non-smoker
- relation extraction : relation between two terms
Their approaches are:
- An ontology-based approach for extracting medical terms of interest
- A graph-based approach which uses the parsing result of link-grammar parser for relation-extraction
- they processed negation
- When the parser fails, they used a pattern-based approach
- because the parser does not process multi-word terms, they
- an NLP-based feature extraction method coupled with an ID3-based decision tree for text classification
This approach was fairly successful
However, the system was tested on the data written by only a clinician, which means that the style of free-text records is consistent. the size of data set used is small. When more diversified writing styles are introduced into patient records, the performance may degrade. link grammar parser makes many errors.
Related papers
The widely cited 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.
An interesting follow-up paper is Turney and Littman, TOIS 2003 which focuses on evaluation of the technique of using PMI for predicting the semantic orientation of words.
