Text Mining of Clinical Records for Cancer Diagnosis

The ability to automatically identify relationships between cancer diseases and external factors from medical records for supporting cancer diagnosis would be a valuable contribution in public health fields. Unfortunately, so far little attention has been paid on such a problem domain to developing effective solutions. In this work, we propose a prototype for automating the extraction of relationships between cancer diseases and potential factors from clinical records. We describe a scheme of the system prototype that integrates cancer ontology, and developed text mining techniques, covering self-organizing maps (SOM) algorithm as well as support vector machines (SVM) methods to carry out the system development. The results show that the integration of medical ontology and the text mining platforms is capable of extracting the potential patterns and re-categorize clinical records.