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EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research
23
Zitationen
7
Autoren
2019
Jahr
Abstract
BACKGROUND: Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this data is often manually extracted for use in research studies. However, due to the large volume of notes in datasets, manually extracting numerical data often becomes infeasible. The objective of this study is to develop and validate a natural language processing (NLP) tool that can efficiently extract numerical clinical data from narrative notes. RESULTS: score of EXTEND ranged from 0.91 to 0.95, 0.95 to 1.0 and 0.95 to 0.96. CONCLUSIONS: EXTEND is an efficient, flexible tool that uses knowledge-based rules to extract clinical numerical parameters with high accuracy. By increasing dictionary terms and developing new rules, the usage of EXTEND can easily be expanded to extract additional numerical data important in clinical outcomes research.
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