Recent years have witnessed dramatic growth of data relating to patient experience. With the sheer volume and velocity of data generated from increasingly diverse sources, and developments in modelling approaches, we are now in a position to be able to ask and answer some of the biggest questions using large-scale data analysis. The projected funded by EU-H2020 aims to develop an automated tool for real-time detection of opinions relating to patient experience which could be potentially useful for timely detection of poor service quality perception. In particular, it will extract information relating to patient feedback and experience, automatically map the extracted opinions into various aspects of healthcare services, discover connections between elements that result in a perception of low and high quality of service and present results in a visual dashboard to facilitate timely interventions.
Please click here for a visualisation of topics extracted from patient reviews.
Dr. Lin Gui, Runcong Zhao
Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du and Ruifeng Xu. Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis. In Proceedings of the 28th International Conference on Computational Linguistics (COLING), 2020. (To appear)
Junru Lu, Gabriele Pergola, Lin Gui, Binyang Li and Yulan He. Cross-passage Hierarchical Memory Network for Generative Review Question Answering. In Proceedings of the 28th International Conference on Computational Linguistics (COLING), 2020. (To appear)
Lin Gui, Jia Leng, Jiyun Zhou, Ruifeng Xu, Yulan He. Multi-Task Learning with Mutual Learning for Joint Sentiment Classification and Topic Detection. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. (impact factor: 4.935)[Code&Data][Paper]
Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Yulan He, Ruifeng Xu. Aspect-Invariant Sentiment Feature Learning: Adversarial Multi-task Learning for Aspect-based Sentiment Analysis. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), 2020. [Paper]
Chuang Fan, Chaofa Yuan, Jiachen Du, Lin Gui, Ruifeng Xu. Transition-based Directed Graph Construction for Emotion-Cause Pair Extraction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2020. [Code&Data][Paper]
Jiacheng Du, Lin Gui, Ruifeng Xu. Commonsense Knowledge Enhanced Memory Network for Stance Classification. IEEE Intelligent Systems, 2020. (impact factor: 3.210) [Paper]
Ruifeng Xu, Zhiyuan Wen, Lin Gui, Qin Lu, Binyang Li, Xizhao Wang. Ensemble with Estimation: Seeking for Optimization in Class Noisy Data. International Journal of Machine Learning and Cybernetics, 2020. (impact factor: 3.753) [Paper]
Chuang Fan, Hongyu Yan, Jiacheng Du, Lin Gui, Lidong Bing, Min Yang, Ruifeng Xu, Ruibin Mao. A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis. EMNLP 2019. [Paper]
Gabriele Pergola, Lin Gui, Yulan He. TDAM: a Topic-Dependent Attention Model for Sentiment Analysis. Journal of Information Processing and Management. 2019. (impact factor: 4.787) [Paper]
Jiyun Zhou, Qin Lu, Lin Gui, Ruifeng Xu, Yunfei Long, Hongpeng Wang. MTTFsite: Cross-cell-type TF Binding Site Prediction by using Multi-task Learning. Bioinformatics, 2019. (impact factor: 5.610) [Code][Paper]
Jiachen Du, Lin Gui, Yulan He, Ruifeng Xu, Xuan Wang. Convolution-Based Neural Attention with Applications to Sentiment Classification. IEEE Access, 2019. [Paper]
|This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 794196.|