Knowledge representation and retrieval using conceptual graphs and free text document self-organisation techniques.
Hospitals generate and store a large amount of clinical data each year, a significant portion of which is in free text format. Conventional database storage and retrieval algorithms are incapable of effectively processing free text medical data. The rich information and knowledge buried in healthcare records are unavailable for clinical decision-making. We examined a number of techniques for structuring and processing free text documents to effective and efficient for information retrieval and knowledge discovery. One critical success criterion is that the complexity of the techniques must be polynomial both in space and time for them to be able to cope with very large databases. We used conceptual graphs (CG) to capture the structure and semantic information/knowledge contained within the free text medical documents. Ordering and self-organising techniques (lattice techniques and knowledge space) were used to improve organisation of concepts from standard medical nomenclatures and large sets of free text medical documents. Pair-wise union of CG was performed to identify the common generalisation structure and a lattice structure of these CG documents. A combination of all three techniques allowed us to organise a set of 9000 discharge summaries into a generalisation hierarchy that supported efficient and rich information/knowledge retrieval.
Chu
S
S
Centre of Medical Informatics, Institute of Public Health, Monash Medical Centre, 246 Clayton Road, Clayton, Victoria 3168, Australia. branko.cesnick@med.monash.edu.au
Cesnik
B
B
D002965:Classification; D006751:Hospital Information Systems; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008491:Medical Informatics Applications; D009323:Natural Language Processing; D018875:Vocabulary, Controlled