Inferring new relations between medical entities using literature curated term co-occurrences.

Content Provider	Europe PMC
Author	Spiro, Adam Fernández García, Jonatan Yanover, Chen
Copyright Year	2019
Abstract	AbstractObjectivesIdentifying new relations between medical entities, such as drugs, diseases, and side effects, is typically a resource-intensive task, involving experimentation and clinical trials. The increased availability of related data and curated knowledge enables a computational approach to this task, notably by training models to predict likely relations. Such models rely on meaningful representations of the medical entities being studied. We propose a generic features vector representation that leverages co-occurrences of medical terms, linked with PubMed citations.Materials and MethodsWe demonstrate the usefulness of the proposed representation by inferring two types of relations: a drug causes a side effect and a drug treats an indication. To predict these relations and assess their effectiveness, we applied 2 modeling approaches: multi-task modeling using neural networks and single-task modeling based on gradient boosting machines and logistic regression.ResultsThese trained models, which predict either side effects or indications, obtained significantly better results than baseline models that use a single direct co-occurrence feature. The results demonstrate the advantage of a comprehensive representation.DiscussionSelecting the appropriate representation has an immense impact on the predictive performance of machine learning models. Our proposed representation is powerful, as it spans multiple medical domains and can be used to predict a wide range of relation types.ConclusionThe discovery of new relations between various medical entities can be translated into meaningful insights, for example, related to drug development or disease understanding. Our representation of medical entities can be used to train models that predict such relations, thus accelerating healthcare-related discoveries.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6951958&blobtype=pdf
Page Count	8
Journal	JAMIA Open
Volume Number	2
DOI	10.1093/jamiaopen/ooz022
PubMed Central reference number	PMC6951958
Issue Number	3
PubMed reference number	31984370
e-ISSN	25742531
Language	English
Publisher	Oxford University Press
Publisher Date	2019-07-01
Access Restriction	Open
Rights License	This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association.
Subject Keyword	machine learning medical informatics MeSH headings literature-based discovery adverse drug reaction drug repositioning
Content Type	Text
Resource Type	Article
Subject	Health Informatics