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 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
- extraction of associated medical concepts
- e.g. Blood pressure & 144/90 in the sentence, "Blood pressure is 144/90"
- text classification
- e.g. 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
- They included the processing of negation.
- When the parser fails, they used a pattern-based approach.
- Because the parser did not process multi-word terms, they replaced the terms with placeholders.
- an NLP-based feature extraction method coupled with an ID3-based decision tree 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.
