Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

SALTClass: classifying clinical short notes using background knowledge from unlabeled data

Ayoub Bagheri, Daniel Oberski, Arjan Sammani, Peter G.M. van der Heijden, Folkert W. Asselbergs
doi: https://doi.org/10.1101/801944
Ayoub Bagheri
1Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands
2Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: a.bagheri@uu.nl
Daniel Oberski
1Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Arjan Sammani
2Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Peter G.M. van der Heijden
1Department of Methodology and Statistics, Faculty of Social Sciences, Utrecht University, Utrecht, the Netherlands
3Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Folkert W. Asselbergs
2Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
4Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, United Kingdom
5Health Data Research UK and Institute of Health Informatics, University College London, London, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Background With the increasing use of unstructured text in electronic health records, extracting useful related information has become a necessity. Text classification can be applied to extract patients’ medical history from clinical notes. However, the sparsity in clinical short notes, that is, excessively small word counts in the text, can lead to large classification errors. Previous studies demonstrated that natural language processing (NLP) can be useful in the text classification of clinical outcomes. We propose incorporating the knowledge from unlabeled data, as this may alleviate the problem of short noisy sparse text.

Results The software package SALTClass (short and long text classifier) is a machine learning NLP toolkit. It uses seven clustering algorithms, namely, latent Dirichlet allocation, K-Means, MiniBatchK-Means, BIRCH, MeanShift, DBScan, and GMM. Smoothing methods are applied to the resulting cluster information to enrich the representation of sparse text. For the subsequent prediction step, SALTClass can be used on either the original document-term matrix or in an enrichment pipeline. To this end, ten different supervised classifiers have also been integrated into SALTClass. We demonstrate the effectiveness of the SALTClass NLP toolkit in the identification of patients’ family history in a Dutch clinical cardiovascular text corpus from University Medical Center Utrecht, the Netherlands.

Conclusions The considerable amount of unstructured short text in healthcare applications, particularly in clinical cardiovascular notes, has created an urgent need for tools that can parse specific information from text reports. Using machine learning algorithms for enriching short text can improve the representation for further applications.

Availability SALTClass can be downloaded as a Python package from Python Package Index (PyPI) website at https://pypi.org/project/saltclass and from GitHub at https://github.com/bagheria/saltclass.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted October 13, 2019.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
SALTClass: classifying clinical short notes using background knowledge from unlabeled data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
SALTClass: classifying clinical short notes using background knowledge from unlabeled data
Ayoub Bagheri, Daniel Oberski, Arjan Sammani, Peter G.M. van der Heijden, Folkert W. Asselbergs
bioRxiv 801944; doi: https://doi.org/10.1101/801944
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
SALTClass: classifying clinical short notes using background knowledge from unlabeled data
Ayoub Bagheri, Daniel Oberski, Arjan Sammani, Peter G.M. van der Heijden, Folkert W. Asselbergs
bioRxiv 801944; doi: https://doi.org/10.1101/801944

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4667)
  • Biochemistry (10332)
  • Bioengineering (7653)
  • Bioinformatics (26279)
  • Biophysics (13497)
  • Cancer Biology (10663)
  • Cell Biology (15392)
  • Clinical Trials (138)
  • Developmental Biology (8480)
  • Ecology (12800)
  • Epidemiology (2067)
  • Evolutionary Biology (16817)
  • Genetics (11380)
  • Genomics (15451)
  • Immunology (10591)
  • Microbiology (25141)
  • Molecular Biology (10190)
  • Neuroscience (54322)
  • Paleontology (399)
  • Pathology (1663)
  • Pharmacology and Toxicology (2889)
  • Physiology (4332)
  • Plant Biology (9223)
  • Scientific Communication and Education (1585)
  • Synthetic Biology (2552)
  • Systems Biology (6769)
  • Zoology (1459)