Automated Identification of Potential Conflict-of-Interest in Biomedical Articles Using Hybrid Deep Neural Network
Abstract
Conflicts-of-interest (COI) in biomedical research may cause ethical risks, including pro-industry conclusions, restrictions on the behavior of investigators, and the use of biased study designs. To ensure the impartiality and objectivity in research, many journal publishers require authors to provide a COI statement within the body text of their articles at the time of peer-review and publication. However, author’s self-reported COI disclosure often does not explicitly appear in their article, and may not be very accurate or reliable. In this study, we present a two-stage machine learning scheme using a hybrid deep learning neural network (HDNN) that combines a multi-channel convolutional neural network (CNN) and a feed-forward neural network (FNN), to automatically identify a potential COI in online biomedical articles. HDNN is designed to simultaneously learn a syntactic and semantic representation of text, relationships between neighboring words in a sentence, and handcrafted input features, and achieves a better performance overall (accuracy exceeding 96.8%) than other classifiers such as support vector machine (SVM), single/multi-channel CNNs, Long Short-term Memory (LSTM), and an Ensemble model in a series of classification experiments.
Keywords
Conflict-of-interest Two-stage machine learning Hybrid deep neural network MEDLINE®Notes
Acknowledgment
This research was supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health.
References
- 1.
- 2.Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of Advances in Neural Information Processing Systems (NIPS 2013), pp. 3111–3119, Lake Tahoe (2013)Google Scholar
- 3.Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1532–1543, Doha, Qatar (2014)Google Scholar
- 4.Ting, S.L., Ip, W.H., Tsang, A.H.C.: Is Naïve Bayes a good classifier for document classification? Int. J. Softw. Eng. Appl. 5(3), 37–46 (2011)Google Scholar
- 5.Mercer, R.E., Di Marco, C.: A design methodology for a biomedical literature indexing tool using the rhetoric of science. In: Proceedings of the HLT-NAACL 2004 Workshop: BioLINK 2004, Linking Biological Literature, Ontologies and Databases, pp. 77–84, Boston (2004)Google Scholar
- 6.Athar, A.: Sentiment analysis of citations using sentence-structure-based features. In: Proceedings of 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT 2011), pp. 81–87, Portland (2011)Google Scholar
- 7.LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
- 8.Kalchbrenne, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 655–665, Baltimore (2014)Google Scholar
- 9.dos Santos, C.N., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of the 25th International Conference on Computational Linguistics (COLING 2014): Technical Papers, pp. 69–78, Dublin, Ireland (2014)Google Scholar
- 10.Dong, L., Wei, F., Zhou, M., Xu, K.: Question answering over freebase with multi-column convolutional neural networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), pp. 260–269, Beijing, China (2015)Google Scholar
- 11.Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP 2014), pp. 1746–1751, Doha, Qatar (2014)Google Scholar
- 12.Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
- 13.Sutskever, I., Vinyals, O., Le, Q.: Sequence to sequence learning with neural networks. In: Proceedings of Advances in Neural Information Processing Systems (NIPS 2014), pp. 3104–3112, Montreal, Canada (2014)Google Scholar
- 14.Ghosal, D., Bhatnagar, S., Akhtar, M.S., Ekbal, A., Bhattacharyya, P.: IITP at SemEval-2017 task 5: an ensemble of deep learning and feature based models for financial sentiment analysis. In: Proceedings of the 11th International Workshop on Semantic Evaluations, pp. 899–903, Vancouver, Canada (2017)Google Scholar
- 15.
- 16.Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S.J., McClosky, D.: The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60, Baltimore (2014)Google Scholar
- 17.Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Proceedings of Advances in Neural Information Processing Systems (NIPS 2015), pp. 649–657, Montreal, Canada (2015)Google Scholar
- 18.Galavotti, L., Sebastiani, F., Simi, M.: Experiments on the use of feature selection and negative evidence in automated text categorization. In: Borbinha, J., Baker, T. (eds.) ECDL 2000. LNCS, vol. 1923, pp. 59–68. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45268-0_6CrossRefGoogle Scholar
- 19.
- 20.Abadi, M., Agarwal, A. et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. Software (2015). tensorflow.org
- 21.Chollet, F., et al.: Keras. GitHub (2015). https://github.com/fchollet/keras
- 22.Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001). http://www.csie.ntu.edu.tw/~cjlin/libsvm