Using a combination of methodologies for improving medical information retrieval performance

This thesis presents three approaches to improve the current state of Medical Information Retrieval. At the time of this writing, the health industry is experiencing a massive change in terms of introducing technology into all aspects of health delivery. The work in this thesis involves adapting existing established concepts in the field of Information Retrieval to the field of Medical Information Retrieval. In particular, we apply subtype filtering, ICD-9 codes, query expansion, and re-ranking methods in order to improve retrieval on medical texts. The first method applies association rule mining and cosine similarity measures. The second method applies subtype filtering and the Apriori algorithm. And the third method uses ICD-9 codes in order to improve retrieval accuracy. Overall, we show that the current state of medical information retrieval has substantial room for improvement. Our first two methods do not show significant improvements, while our third approach shows an improvement of up to 20%