Journal Article

EXTraction of EMR numerical data: an efficient and generalizable tool to EXTEND clinical research

Katherine P. Liao, Tianrun Cai, Kanako K. Kumamaru, Luwan Zhang, Frank J. Rybicki, Tianxi Cai, Nicole Yang

2019

Overview

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 To validate the accuracy of the tool EXTraction of EMR Numerical Data (EXTEND), we developed a reference standard by manually extracting vital signs from 285 notes, EF values from 300 notes, glycated hemoglobin (HbA1C), and serum creatinine from 890 notes. For each parameter of interest, we calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F 1 score of EXTEND using two metrics. (1) completion of data extraction, and (2) accuracy of data extraction compared to the actual values in the note verified by chart review. At the note level, extraction by EXTEND was considered correct only if it accurately detected and extracted all values of interest in a note. Using manually-annotated labels as the gold standard, the note-level accuracy of EXTEND in capturing the numerical vital sign values, EF, HbA1C and creatinine ranged from 0.88 to 0.95 for sensitivity, 0.95 to 1.0 for specificity, 0.95 to 1.0 for PPV, 0.89 to 0.99 for NPV, and 0.92 to 0.96 in F 1 scores. Compared to the actual value level, the sensitivity, PPV, and F 1 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.

• Publisher: Springer Science and Business Media LLC,BioMed Central,BioMed Central Ltd,BMC
• Subject: Algorithms / Big data / Clinical trials / Computer applications to medicine. Medical informatics / Creatinine / Creatinine - blood / Data extraction / Data mining / Electronic Health Records / Electronic records / EMR / Glycated Hemoglobin / Glycated Hemoglobin A - metabolism / Glycosylated hemoglobin / Health Informatics / Hemoglobins / Humans / Information Storage and Retrieval / Information Systems and Communication Service / machine learning / Management of Computing and Information Systems / Medical records / Medical research / Medicine / Medicine & Public Health / modeling / Natural Language Processing / Numerical data / R858-859.7 / Sensitivity and Specificity / Software / Stroke Volume / technology / Vital Signs