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Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study
- Source :
- JMIR Medical Informatics
- Publication Year :
- 2019
- Publisher :
- JMIR Publications, 2019.
-
Abstract
- Background Research on disease-disease association (DDA), like comorbidity and complication, provides important insights into disease treatment and drug discovery, and a large body of the literature has been published in the field. However, using current search tools, it is not easy for researchers to retrieve information on the latest DDA findings. First, comorbidity and complication keywords pull up large numbers of PubMed studies. Second, disease is not highlighted in search results. Finally, DDA is not identified, as currently no disease-disease association extraction (DDAE) dataset or tools are available. Objective As there are no available DDAE datasets or tools, this study aimed to develop (1) a DDAE dataset and (2) a neural network model for extracting DDA from the literature. Methods In this study, we formulated DDAE as a supervised machine learning classification problem. To develop the system, we first built a DDAE dataset. We then employed two machine learning models, support vector machine and convolutional neural network, to extract DDA. Furthermore, we evaluated the effect of using the output layer as features of the support vector machine-based model. Finally, we implemented large margin context-aware convolutional neural network architecture to integrate context features and convolutional neural networks through the large margin function. Results Our DDAE dataset consisted of 521 PubMed abstracts. Experiment results showed that the support vector machine-based approach achieved an F1 measure of 80.32%, which is higher than the convolutional neural network-based approach (73.32%). Using the output layer of convolutional neural network as a feature for the support vector machine does not further improve the performance of support vector machine. However, our large margin context-aware-convolutional neural network achieved the highest F1 measure of 84.18% and demonstrated that combining the hinge loss function of support vector machine with a convolutional neural network into a single neural network architecture outperforms other approaches. Conclusions To facilitate the development of text-mining research for DDAE, we developed the first publicly available DDAE dataset consisting of disease mentions, Medical Subject Heading IDs, and relation annotations. We developed different conventional machine learning models and neural network architectures and evaluated their effects on our DDAE dataset. To further improve DDAE performance, we propose an large margin context-aware-convolutional neural network model for DDAE that outperforms other approaches.
- Subjects :
- Computer science
Health Informatics
Machine learning
computer.software_genre
Convolutional neural network
03 medical and health sciences
0302 clinical medicine
Health Information Management
Margin (machine learning)
Hinge loss
convolutional neural networks
Feature (machine learning)
biomedical natural language processing
030304 developmental biology
0303 health sciences
Original Paper
Artificial neural network
business.industry
Deep learning
deep learning
Support vector machine
Statistical classification
ComputingMethodologies_PATTERNRECOGNITION
biological relation extraction
030220 oncology & carcinogenesis
disease-disease association
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 22919694
- Volume :
- 7
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- JMIR Medical Informatics
- Accession number :
- edsair.doi.dedup.....9dd7d66fd901e0e04f8a172722cc4b0f