A System for Classifying Disease Comorbidity Status from Medical Discharge Summaries Using Automated Hotspot and Negated Concept Detection

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...

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Bibliographic Details
Published in:Journal of the American Medical Informatics Association : JAMIA 2009-07, Vol.16 (4), p.590-595
Main Authors: Ambert, Kyle H., Cohen, Aaron M.
Format: Article
Language:English
Subjects:
Classification - methods
Comorbidity
Disease - classification
Humans
Information Storage and Retrieval - methods
Medical Records Systems, Computerized
Natural Language Processing
Obesity
Patient Discharge
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Summary: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. 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. Performance was evaluated in terms of the macroaveraged F1 measure. 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. The system demonstrates that effective comorbidity status classification by an automated system is possible.
ISSN:1067-5027
1527-974X
DOI:10.1197/jamia.M3095