Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives
Name
journal.pone.0192360.pdf
Size
2.15 MB
Format
Adobe PDF
Checksum (MD5)
e04ff605d56112f64cd7f68d27348eb3
Author(s)
Gehrmann, Sebastian
Li, Yeran
Carlson, Eric T.
Wu, Joy T.
Welt, Jonathan
Foote, John
Moseley, Edward T.
Grant, David W.
Tyler, Patrick D.
Dernoncourt, Franck
Date Issued
February 2018
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Gehrmann, Sebastian et al. “Comparing Deep Learning and Concept Extraction Based Methods for Patient Phenotyping from Clinical Narratives.” Edited by Jen-Hsiang Chuang. PLOS ONE 13, 2 (February 2018): e0192360 © 2018 Gehrmann et al
Version
Final published version
Abstract
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text classification can augment the existing techniques by leveraging the representation of language to learn which phrases in a text are relevant for a given medical condition. In this work, we compare concept extraction based methods with CNNs and other commonly used models in NLP in ten phenotyping tasks using 1,610 discharge summaries from the MIMIC-III database. We show that CNNs outperform concept extraction based methods in almost all of the tasks, with an improvement in F1-score of up to 26 and up to 7 percentage points in area under the ROC curve (AUC). We additionally assess the interpretability of both approaches by presenting and evaluating methods that calculate and extract the most salient phrases for a prediction. The results indicate that CNNs are a valid alternative to existing approaches in patient phenotyping and cohort identification, and should be further investigated. Moreover, the deep learning approach presented in this paper can be used to assist clinicians during chart review or support the extraction of billing codes from text by identifying and highlighting relevant phrases for various medical conditions.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
MIT Critical Data (Laboratory)
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0192360
