{"type":"ScholarlyArticle","title":"Cooperative Joint Attentive Network for Patient Outcome Prediction on Irregular Multi-Rate Multivariate Health Data","authors":[{"name":"Qingxiong Tan","slug":"qingxiong-tan","given":"Qingxiong","family":"Tan","affiliation":""},{"name":"Mang Ye","slug":"mang-ye","given":"Mang","family":"Ye","affiliation":""},{"name":"Grace Lai-Hung Wong","slug":"grace-lai-hung-wong","given":"Grace Lai-Hung","family":"Wong","affiliation":""},{"name":"PongChi Yuen","slug":"pongchi-yuen","given":"PongChi","family":"Yuen","affiliation":""}],"abstract":"Due to the dynamic health status of patients and discrepant stability of physiological variables, health data often presents as irregular multi-rate multivariate time series (IMR-MTS) with significantly varying sampling rates. Existing methods mainly study changes of IMR-MTS values in the time domain, without considering their different dominant frequencies and varying data quality. Hence, we propose a novel Cooperative Joint Attentive Network (CJANet) to analyze IMR-MTS in frequency domain, which adaptively handling discrepant dominant frequencies while tackling diverse data qualities caused by irregular sampling. In particular, novel dual-channel joint attention is designed to jointly identify important magnitude and phase signals while detecting their dominant frequencies, automatically enlarging the positive influence of key variables and frequencies. Furthermore, a new cooperative learning module is introduced to enhance information exchange between magnitude and phase channels, effectively integrating global signals to optimize the network. A frequency-aware fusion strategy is finally designed to aggregate the learned features. Extensive experimental results on real-world medical datasets indicate that CJANet significantly outperforms existing methods and provides highly interpretable results.","doi":"10.24963/ijcai.2021/219","url":"https://ijcai.org/proceedings/2021/219","pdf":"https://ijcai.org/proceedings/2021/0219.pdf","markdown":"https://ijcai.org/proceedings/2021/219.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/219","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Data Mining","track":"Main Track","number":219,"pages":{"first":1586,"last":1592},"keywords":["Data Mining: Mining Spatial, Temporal Data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}