TY - JOUR
T1 - Using a large margin context-aware convolutional neural network to automatically extract disease-disease association from literature
T2 - Comparative analytic study
AU - Lai, Po Ting
AU - Lu, Wei Liang
AU - Kuo, Ting Rung
AU - Chung, Chia Ru
AU - Han, Jen Chieh
AU - Tsai, Richard Tzong Han
AU - Horng, Jorng Tzong
N1 - Publisher Copyright:
©Po-Ting Lai, Wei-Liang Lu, Ting-Rung Kuo, Chia-Ru Chung, Jen-Chieh Han, Richard Tzong-Han Tsai, Jorng-Tzong Horng.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - Biological relation extraction
KW - Biomedical natural language processing
KW - Convolutional neural networks
KW - Deep learning
KW - Disease-disease association
UR - http://www.scopus.com/inward/record.url?scp=85118785459&partnerID=8YFLogxK
U2 - 10.2196/14502
DO - 10.2196/14502
M3 - 期刊論文
AN - SCOPUS:85118785459
SN - 2291-9694
VL - 7
JO - JMIR Medical Informatics
JF - JMIR Medical Informatics
IS - 4
M1 - e14502
ER -