Efficient Classification of Clinical Reports Utilizing Natural Language Processing

The recent emphasis on health information technology has highlighted the importance of leveraging the large amount of electronic clinical data to help guide medical decision-making. Developing such clinical decision aids requires manual review of many past patient reports in order to generate a good predictive model. In this research, we investigate classification of clinical reports using natural language processing (NLP). The proposed system uses NLP to generate structured output from computed tomography (CT) reports and then machine learning techniques to code for the presence of clinically important injuries for traumatic orbital fracture victims. Our results show that NLP improves upon raw text classification results.