Named Entity Recognition for the Extraction of Emerging Technological Knowledge from Medical Literature

Sabrina Lamberth-Cocca, Bernhard Maier, Christian Nawroth, Paul Mc Kevitt, Matthias Hemmje

2022

Abstract

In this paper, we show the results of an experimental Information Retrieval System (IRS) prototype to support the detection of emerging medical technology using the method of Named-Entity Recognition (NER). The overall goal is to automatically identify and classify entities and structures in scientific medical articles, which represent the concept of Medical Technologies (MedTech) with high topicality. As a first approach, we combine learning-based NER with rule-based emerging Named-Entity Recognition (eNER). We train a machine-learning model on manually annotated NER candidates representing medical devices. We then match the results with entries from vocabularies containing medical devices according to our definition, using a handcrafted rule-based approach and fuzzy functions. The main outcome is an experimental prototype which we call, MedTech-eNER-IRS, which shows that such an approach works in general, including pointers for further research and prototype improvements.

Paper Citation

in Harvard Style

Lamberth-Cocca S., Maier B., Nawroth C., Mc Kevitt P. and Hemmje M. (2022). Named Entity Recognition for the Extraction of Emerging Technological Knowledge from Medical Literature. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 2: KEOD; ISBN 978-989-758-614-9, SciTePress, pages 101-108. DOI: 10.5220/0011369300003335

in Bibtex Style

@conference{keod22,
author={Sabrina Lamberth-Cocca and Bernhard Maier and Christian Nawroth and Paul Mc Mc Kevitt and Matthias Hemmje},
title={Named Entity Recognition for the Extraction of Emerging Technological Knowledge from Medical Literature},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 2: KEOD},
year={2022},
pages={101-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011369300003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 2: KEOD
TI - Named Entity Recognition for the Extraction of Emerging Technological Knowledge from Medical Literature
SN - 978-989-758-614-9
AU - Lamberth-Cocca S.
AU - Maier B.
AU - Nawroth C.
AU - Mc Kevitt P.
AU - Hemmje M.
PY - 2022
SP - 101
EP - 108
DO - 10.5220/0011369300003335
PB - SciTePress