Abstract
We evaluate the performance of multiple text classification
methods used to automate the screening of article abstracts in
terms of their relevance to a topic of interest. The aim is to
develop a system that can be first trained on a set of manually
screened article abstracts before using it to identify additional
articles on the same topic. Here the focus is on articles related
to the topic artificial intelligence in nursing. Eight text
classification methods are tested, as well as two simple
ensemble systems. The results indicate that it is feasible to use
text classification technology to support the manual screening
process of article abstracts when conducting a literature
review. The best results are achieved by an ensemble system,
which achieves a F1-score of 0.41, with a sensitivity of 0.54 and
a specificity of 0.96. Future work directions are discussed.
Originalsprache | Englisch |
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Titel | MEDINFO 2021: One World, One Health–Global Partnership for Digital Innovation |
Redakteure/-innen | Paula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing |
Herausgeber (Verlag) | IOS Press |
Seiten | 637-640 |
Seitenumfang | 4 |
ISBN (Print) | 978-1-64368-264-8 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2022 |
Research Field
- Exploration of Digital Health
Schlagwörter
- Natural Language Processing; Nursing; Review