Authors:
Sabrina Lamberth-Cocca
1
;
Bernhard Maier
1
;
Christian Nawroth
1
;
Paul Mc Mc Kevitt
2
and
Matthias Hemmje
1
Affiliations:
1
Faculty of Mathematics and Computer Science, University of Hagen, Germany
;
2
Academy for International Science & Research (AISR), Derry/Londonderry, Northern Ireland
Keyword(s):
Named Entity Recognition, Natural Language Processing, Information Retrieval, Knowledge Extraction, Machine Learning, Emerging Medical Technology, Clinical Argumentation Support.
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.