CC BY-NC-ND 4.0 · Yearb Med Inform 2020; 29(01): 208-220
DOI: 10.1055/s-0040-1702001
Section 10: Natural Language Processing
Survey
Georg Thieme Verlag KG Stuttgart

Medical Information Extraction in the Age of Deep Learning

Udo Hahn
1  Jena University Language & Information Engineering (JULIE) Lab, Friedrich-Schiller-Universität Jena, Jena, Germany
,
Michel Oleynik
2  Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
› Author Affiliations
Further Information

Publication History

Publication Date:
21 August 2020 (online)

Summary

Objectives: We survey recent developments in medical Information Extraction (IE) as reported in the literature from the past three years. Our focus is on the fundamental methodological paradigm shift from standard Machine Learning (ML) techniques to Deep Neural Networks (DNNs). We describe applications of this new paradigm concentrating on two basic IE tasks, named entity recognition and relation extraction, for two selected semantic classes—diseases and drugs (or medications)—and relations between them.

Methods: For the time period from 2017 to early 2020, we searched for relevant publications from three major scientific communities: medicine and medical informatics, natural language processing, as well as neural networks and artificial intelligence.

Results: In the past decade, the field of Natural Language Processing (NLP) has undergone a profound methodological shift from symbolic to distributed representations based on the paradigm of Deep Learning (DL). Meanwhile, this trend is, although with some delay, also reflected in the medical NLP community. In the reporting period, overwhelming experimental evidence has been gathered, as illustrated in this survey for medical IE, that DL-based approaches outperform non-DL ones by often large margins. Still, small-sized and access-limited corpora create intrinsic problems for data-greedy DL as do special linguistic phenomena of medical sublanguages that have to be overcome by adaptive learning strategies.

Conclusions: The paradigm shift from (feature-engineered) ML to DNNs changes the fundamental methodological rules of the game for medical NLP. This change is by no means restricted to medical IE but should also deeply influence other areas of medical informatics, either NLP- or non-NLP-based.