A methodology to integrate clinical data for the efficient assessment of brain-tumor patients

Abstract

Careful examination of the medical record of brain-tumor patients can be an overwhelming task for the neuroradiologist. The number of clinical documents alone may approach 100 for a patient that has a 3-year-old brain tumor. The neuroradiologist's evaluation of a patient's brain tumor involves examining the current imaging exam and checking for previous imaging exams that may occur pre- or post-treatment. The goal of this research is to develop an effective method to review all of the pertinent patient information from the medical record. We have designed and developed a medical system that incorporates Hospital Information Systems, Radiology Information Systems, and Picture Archiving and Communications Systems information. Our research improves clinical review of patient's data by organizing image display, removing unnecessary documents, and mining for key clinical scenarios that are important in the assessment and care of brain-tumor patients.

Keywords:

• Data mining
• document classification
• hanging protocol
• maximum entropy
• naïve Bayes
• SVM

Acknowledgements

The medical informatics research staff consisting of Patrick Langdon and Kerry Engler contributed significantly to this project by managing the workstation experiments and gathering the data analyzed in this paper. This research is being supported in part by the National Institute of Health Grant EB000362.