2020 MedicalInformationExtractionint

• (Hahn & Oleynik, 2020) ⇒ Udo Hahn, and Michel Oleynik. (2020). “Medical Information Extraction in the Age of Deep Learning.” In: Yearbook of medical informatics, 29(01).

Subject Headings: Medical Information Extraction.

Cited By

• http://scholar.google.com/scholar?q=%222020%22+Medical+Information+Extraction+in+the+Age+of+Deep+Learning

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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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2020 MedicalInformationExtractionint	Udo Hahn Michel Oleynik			Medical Information Extraction in the Age of Deep Learning						2020