Extracting alcohol and substance abuse status from clinical notes: The added value of nursing data

By: Topaz M., Murga L., Bar-Bachar O., Cato K., Collins S.
Published in: Studies in Health Technology and Informatics
SDGs : SDG 03  |  Units:   | Time: 2019 |  Link
Description: We applied an open source natural language processing (NLP) system “NimbleMiner” to identify clinical notes with mention s of alcohol and substance abuse. NimbleMiner allows users to rapidly discover clinical vocabularies (using word embedding model) and then implement machine learning for text classification. We used a large inpatient dataset with over 50,000 intensive care unit admissions (MIMIC II). Clinical notes included physician-written discharge summaries (n = 51,201) and nursing notes (n = 412,343). We first used physician-written discharge summaries to train the system’s algorithm and then added nursing notes to the physician-written discharge summaries and evaluated algorithms prediction accuracy. Adding nursing notes to the physician-written discharge summaries resulted in almost two-fold vocabulary expansion. NimbleMiner slightly outperformed other state-of-the-art NLP systems (average F-score = .84), while requiring significantly less time for the algorithms development.: Our findings underline the importance of nursing data for the analysis of electronic patient records. © 2019 International Medical Informatics Association (IMIA) and IOS Press.