A System for Classifying Disease Comorbidity Status from Medical Discharge Summaries Using Automated Hotspot and Negated Concept Detection
- Kyle H Ambert,
- Aaron M Cohen, MD, MS
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR
- Correspondence: Kyle H. Ambert, Oregon Health & Science University, 3181 SW Sam Jackson Pk Rd, Mailcode: BICC, Portland, OR 97239; e-mail: <ambertk{at}ohsu.edu>
- Received 5 December 2008
- Accepted 7 April 2009
Abstract
Objective Free-text clinical reports serve as an important part of patient care management and clinical documentation of patient disease and treatment status. Free-text notes are commonplace in medical practice, but remain an under-used source of information for clinical and epidemiological research, as well as personalized medicine. The authors explore the challenges associated with automatically extracting information from clinical reports using their submission to the Integrating Informatics with Biology and the Bedside (i2b2) 2008 Natural Language Processing Obesity Challenge Task.
Design A text mining system for classifying patient comorbidity status, based on the information contained in clinical reports. The approach of the authors incorporates a variety of automated techniques, including hot-spot filtering, negated concept identification, zero-vector filtering, weighting by inverse class-frequency, and error-correcting of output codes with linear support vector machines.
Measurements Performance was evaluated in terms of the macroaveraged F1 measure.
Results The automated system performed well against manual expert rule-based systems, finishing fifth in the Challenge's intuitive task, and 13th in the textual task.
Conclusions The system demonstrates that effective comorbidity status classification by an automated system is possible.
