Objective: This paper describes a new congestive heart failure (CHF) treatment performance measure information extraction system -CHIEF -developed as part of the Automated Data Acquisition for Heart Failure project, a Veterans Health Administration project aiming at improving the detection of patients not receiving recommended care for CHF. Design: CHIEF is based on the Apache Unstructured Information Management Architecture framework, and uses a combination of rules, dictionaries, and machine learning methods to extract left ventricular function mentions and values, CHF medications, and documented reasons for a patient not receiving these medications. Measurements: The training and evaluation of CHIEF were based on subsets of a reference standard of various clinical notes from 1083 Veterans Health Administration patients. Domain experts manually annotated these notes to create our reference standard. Metrics used included recall, precision, and the F 1 -measure. Conclusion: This study demonstrated that applying natural language processing to unlock the rich and detailed clinical information found in clinical narrative text notes makes fast and scalable quality improvement approaches possible, eventually improving management and outpatient treatment of patients suffering from CHF.
Meystre et al. REF proposed a new IE system for a congestive heart failure performance measure based on clinical notes from 1083 Veterans Health Administration patients.
Congestive heart failure information extraction framework for automated treatment performance measures assessment
Journal of the American Medical Informatics Association
Computer Science Medicine