Novel Nature Inspired Techniques in Medical Data Mining
Miroslav Burša, Lenka Lhotská · Procedia Computer Science · 2011
In this work we have studied, evaluated and proposed different swarm intelligence techniques for mining information from loosely structured medical textual records with no a priori knowledge (a large dataset). The output of this task is a set of ordered/nominal attributes suitable for rule discovery mining. Information mining from textual data becomes a very challenging task when the structure of the text record is loose without any rules. The task becomes even harder when natural language is used and no a priori knowledge is available. First, classical approaches such as basic statistic approaches, single and multiple word frequency analysis, etc., have been used to simplify the textual data and provide an overview of the data. Finally, an ant-inspired self-learning approach has been used to automatically provide a simplified dominant structure, presenting structure of the records in the human readable form that can be further utilized in the mining process as it describes the vast majority of the records. Note that this project is an ongoing process (and research) and new data are irregularly received from the medical facility, justifying the need for robust and fool-proof algorithms.