{"type":"ScholarlyArticle","title":"Pairwise-Ranking based Collaborative Recurrent Neural Networks for Clinical Event Prediction","authors":[{"name":"Zhi Qiao","slug":"zhi-qiao","given":"Zhi","family":"Qiao","affiliation":"* IBM Research -China , Beijing , China"},{"name":"Shiwan Zhao","slug":"shiwan-zhao","given":"Shiwan","family":"Zhao","affiliation":"* IBM Research -China , Beijing , China"},{"name":"Cao Xiao","slug":"cao-xiao","given":"Cao","family":"Xiao","affiliation":"† IBM Research , Cambridge , MA , 02142"},{"name":"Xiang Li","slug":"xiang-li","given":"Xiang","family":"Li","affiliation":"* IBM Research -China , Beijing , China"},{"name":"Yong Qin","slug":"yong-qin","given":"Yong","family":"Qin","affiliation":"* IBM Research -China , Beijing , China"},{"name":"Fei Wang","slug":"fei-wang","given":"Fei","family":"Wang","affiliation":"¶ Healthcare Policy and Research , Weill Cornell Medical College , Cornell University , New York , NY 10065"}],"abstract":"Patient Electronic Health Records (EHR) data consist of sequences of patient visits over time. Sequential prediction of patients' future clinical events (e.g., diagnoses) from their historical EHR data is a core research task and motives a series of predictive models including deep learning. The existing research mainly adopts a classification framework, which treats the observed and unobserved events as positive and negative classes. However, this may not be true in real clinical setting considering the high rate of missed diagnoses and human errors. In this paper, we propose to formulate the clinical event prediction problem as an events recommendation problem. An end-to-end pairwise-ranking based collaborative recurrent neural networks (PacRNN) is proposed to solve it, which firstly embeds patient clinical contexts with attention RNN, then uses Bayesian Personalized Ranking (BPR) regularized by disease co-occurrence to rank probabilities of patient-specific diseases, as well as use point process to provide simultaneous prediction of the occurring time of these diagnoses. Experimental results on two real world EHR datasets demonstrate the robust performance, interpretability, and efficacy of PacRNN.","doi":"10.24963/ijcai.2018/489","url":"https://ijcai.org/proceedings/2018/489","pdf":"https://ijcai.org/proceedings/2018/0489.pdf","markdown":"https://ijcai.org/proceedings/2018/489.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/489","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":489,"pages":{"first":3520,"last":3526},"keywords":["Machine Learning: Deep Learning","Machine Learning Applications: Bio;Medicine","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In Proceed- ings of the 3rd International Conference on Learning Representations (ICLR), 2015.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2015","venue":"Proceedings of the 3rd International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baytas et al., 2017] Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, and Jiayu Zhou. Patient subtyp- ing via time-aware lstm networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining (KDD), 2017.","title":"Patient subtyping via time-aware lstm networks","authors":"Inci M Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K Jain, Jiayu Zhou","year":"2017","venue":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cheng et al., 2016] Yu Cheng, Fei Wang, Ping Zhang, and Jianying Hu. Risk prediction with electronic health records: A deep learning approach. In Proceedings of SIAM International Conference on Data Mining (SDM), 2016.","title":"Risk prediction with electronic health records: A deep learning approach","authors":"Yu Cheng, Fei Wang, Ping Zhang, Jianying Hu","year":"2016","venue":"Proceedings of SIAM International Conference on Data Mining (SDM)","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cho et al., 2014] Kyunghyun Cho, Bart van Merriënboer, C ¸aglar Gülc ¸ehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. Learning phrase representations using rnn encoder-decoder for statisti- cal machine translation. In Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.","title":"Learning phrase representations using rnn encoder-decoder for statistical machine translation","authors":"Cho","year":"2014","venue":"Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Choi et al., 2016a] Edward Choi, Mohammad Taha Ba- hadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. Doctor ai: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (MLHC), 2016.","title":"Doctor ai: Predicting clinical events via recurrent neural networks","authors":"Choi","year":"2016","venue":"Proceedings of the 1st Machine Learning for Healthcare Conference (MLHC)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Choi et al., 2016b] Edward Choi, Mohammad Taha Ba- hadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. Retain: An interpretable predictive model for healthcare using reverse time attention mechanism. In Proceedings of Advances in Neural Information Process- ing Systems (NIPS). 2016.","title":"Retain: An interpretable predictive model for healthcare using reverse time attention mechanism","authors":"Choi","year":"2016","venue":"Proceedings of Advances in Neural Information Processing Systems (NIPS)","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Choi et al., 2016c] Edward Choi, Andy Schuetz, Walter F Stewart, and Jimeng Sun. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 2016.","title":"Using recurrent neural network models for early detection of heart failure onset","authors":"Choi","year":"2016","venue":"Journal of the American Medical Informatics Association","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Donald et al., 1982] Connelly Donald, Benson Ellis, and Burke M. Clinical decisions and laboratory use. In Min- neapolis: University of Minnesota Press, 1982.","title":"Clinical decisions and laboratory use","authors":"Connelly Donald, Benson Ellis, Burke M","year":"1982","venue":"Minneapolis","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Du et al., 2016] Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song. Recurrent marked temporal point processes: Embedding event history to vector. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining (KDD), 2016.","title":"Recurrent marked temporal point processes: Embedding event history to vector","authors":"Nan Du, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, Le Song","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Farhan et al., 2016] Wael Farhan, Zhimu Wang, Yingxiang Huang, Shuang Wang, Fei Wang, and Xiaoqian Jiang. A predictive model for medical events based on contextual embedding of temporal sequences. JMIR Medical Infor- matics, 4(4), 2016.","title":"A predictive model for medical events based on contextual embedding of temporal sequences","authors":"Wael Farhan, Zhimu Wang, Yingxiang Huang, Shuang Wang, Fei Wang, Xiaoqian Jiang","year":"2016","venue":"JMIR Medical Informatics","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hawkes, 1971] Alan G. Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83-90, 1971.","title":"Spectra of some self-exciting and mutually exciting point processes","authors":"Alan G Hawkes","year":"1971","venue":"Biometrika","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Isaacson, 1989] Eugene Isaacson. Numerical recipes in c: The art of scientific computing. SIAM Review, 31(1):142- 142, 1989.","title":"Numerical recipes in c: The art of scientific computing","authors":"Eugene Isaacson, Isaacson","year":"1989","venue":"SIAM Review","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li, 2011] Hang Li. A short introduction to learning to rank. IEICE Transactions, 94-D(10):1854-1862, 2011.","title":"A short introduction to learning to rank","authors":"Hang Li","year":"2011","venue":"IEICE Transactions","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2015] Chuanren Liu, Fei Wang, Jianying Hu, and Hui Xiong. Temporal phenotyping from longitudinal elec- tronic health records: A graph based framework. In Pro- ceedings of the 21st ACM SIGKDD International Confer- ence on Knowledge Discovery and Data Mining (KDD), 2015.","title":"Temporal phenotyping from longitudinal electronic health records: A graph based framework","authors":"Chuanren Liu, Fei Wang, Jianying Hu, Hui Xiong","year":"2015","venue":"Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ma et al., 2017] Fenglong Ma, Radha Chitta, Jing Zhou, Quanzeng You, Tong Sun, and Jing Gao. Dipole: Diag- nosis prediction in healthcare via attention-based bidirec- tional recurrent neural networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining (KDD), 2017.","title":"Dipole: Diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks","authors":"Ma","year":"2017","venue":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Qiao et al., 2014] Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo, and Binxing Fang. Combining het- erogenous social and geographical information for event recommendation. In Proceedings of AAAI Conference on Artificial Intelligence (AAAI), 2014.","title":"Combining heterogenous social and geographical information for event recommendation","authors":"Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo, Binxing Fang","year":"2014","venue":"Proceedings of AAAI Conference on Artificial Intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Ar- tificial Intelligence (UAI), 2009.","title":"BPR: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI)","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Sebastian et al., 2009] Köhler Sebastian, H. Schulz Marcel, Krawitz Peter, Bauer Sebastian, Dölken Sandra, E. Ott Claus, Mundlos Christine, Horn Denise, Mundlos Stefan, and N. Robinson Peter. Clinical diagnostics in human ge- netics with semantic similarity searches in ontologies. In American Journal of Human Genetics, 2009.","title":"Clinical diagnostics in human genetics with semantic similarity searches in ontologies","authors":"H Köhler Sebastian, Schulz, Krawitz Marcel, Bauer Peter, Dölken Sebastian, E Sandra, Mundlos Ott Claus, Christine, Denise Horn, Mundlos Stefan, N Robinson Peter","year":"2009","venue":"American Journal of Human Genetics","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tengfei et al., 2018] Ma Tengfei, Cao Xiao, and Fei Wang. Health-atm: A deep architecture for multifaceted patient health record representation and risk prediction. In Pro- ceedings of SIAM International Conference on Data Min- ing (SDM), 2018.","title":"Health-atm: A deep architecture for multifaceted patient health record representation and risk prediction","authors":"Ma Tengfei, Cao Xiao, Fei Wang","year":"2018","venue":"Proceedings of SIAM International Conference on Data Mining (SDM)","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Xiao et al., 2017] Shuai Xiao, Junchi Yan, Xiaokang Yang, Hongyuan Zha, and Stephen M. Chu. Modeling the in- tensity function of point process via recurrent neural net- works. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI), 2017.","title":"Modeling the intensity function of point process via recurrent neural networks","authors":"Shuai Xiao, Junchi Yan, Xiaokang Yang, Hongyuan Zha, Stephen M Chu","year":"2017","venue":"Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yuan et al., 2011] Quan Yuan, Li Chen, and Shiwan Zhao. Factorization vs. regularization: Fusing heterogeneous so- cial relationships in top-n recommendation. In Proceed- ings of 5th ACM Conference on Recommender Systems (RecSys), 2011.","title":"Factorization vs. regularization: Fusing heterogeneous social relationships in top-n recommendation","authors":"Quan Yuan, Li Chen, Shiwan Zhao","year":"2011","venue":"Proceedings of 5th ACM Conference on Recommender Systems (RecSys)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}