Longitudinal analysis of new information types in clinical notes.
Year: 2014
Type: article
Abstract: It is increasingly recognized that redundant information in clinical notes within electronic health record (EHR) systems is ubiquitous, significant, and may negatively impact the secondary use of these notes for research and patient care. We investigated several automated methods to identify redundant versus relevant new information in clinical reports. These methods may provide a valuable approach to extract clinically pertinent information and further improve the accuracy of clinical information extraction systems. In this study, we used UMLS semantic types to extract several types of new information, including problems, medications, and laboratory information. Automatically identified new information highly correlated with manual reference standard annotations. Methods to identify different types of new information can potentially help to build up more robust information extraction systems for clinical researchers as well as aid clinicians and researchers in navigating clinical notes more effectively and quickly identify information pertaining to changes in health states.
Source: PubMed
Authors: Rui Zhang, Serguei Pakhomov, Genevieve B Melton
Institutions: University of Minnesota, University of Minnesota System
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W158518493