Use of Natural Language Processing for Precise Retrieval of Key Elements of Health IT Evaluation Studies

Abstract:

Having precise information about health IT evaluation studies is important for evidence-based decisions in medical informatics. In a former feasibility study, we used a faceted search based on ontological modeling of key elements of studies to retrieve precisely described health IT evaluation studies. However, extracting the key elements manually for the modeling of the ontology was time and resource-intensive. We now aimed at applying natural language processing to substitute manual data extraction by automatic data extraction. Four methods (Named Entity Recognition, Bag-of-Words, Term-Frequency-Inverse-Document-Frequency, and Latent Dirichlet Allocation Topic Modeling were applied to 24 health IT evaluation studies. We evaluated which of these methods was best suited for extracting key elements of each study. As gold standard, we used results from manual extraction. As a result, Named Entity Recognition is promising but needs to be adapted to the existing study context. After the adaption, key elements of studies could be collected in a more feasible, time- and resource-saving way.

DOI: 10.3233/SHTI200502

Projects: Management of health information systems

Publication type: Journal article

Journal: Studies in health technology and informatics

Human Diseases: No Human Disease specified

Citation: Studies in health technology and informatics 272:95–98

Date Published: 2020

Registered Mode: imported from a bibtex file

Authors: Verena Dornauer, Franziska Jahn, Konrad Hoeffner, Alfred Winter, Elske Ammenwerth

Help
help Submitter
Citation
Dornauer Verena, Jahn Franziska, Hoeffner Konrad, Winter Alfred, & Ammenwerth Elske. (2020). Use of Natural Language Processing for Precise Retrieval of Key Elements of Health IT Evaluation Studies [JB]. Studies in Health Technology and Informatics, 272(The Importance of Health Informatics in Public Health during a Pandemic), 95–98. https://doi.org/10.3233/SHTI200502
Activity

Views: 1713

Created: 10th Nov 2020 at 14:58

Last updated: 7th Dec 2021 at 17:58

help Tags

This item has not yet been tagged.

help Attributions

None

Related items

Powered by
(v.1.13.0-master)
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies