{"type":"ScholarlyArticle","title":"R2V-MIF: Rule-to-Vector Contrastive Learning and Multi-channel Information Fusion for Therapy Recommendation","authors":[{"name":"Nengjun Zhu","slug":"nengjun-zhu","given":"Nengjun","family":"Zhu","affiliation":"School of Computer Engineering and Science , Shanghai University"},{"name":"Jieyun Huang","slug":"jieyun-huang","given":"Jieyun","family":"Huang","affiliation":"School of Computer Engineering and Science , Shanghai University"},{"name":"Jian Cao","slug":"jian-cao","given":"Jian","family":"Cao","affiliation":"Department of Computer Science and Engineering , Shanghai Jiao Tong University"},{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"Tongji University"},{"name":"Zixuan Yuan","slug":"zixuan-yuan","given":"Zixuan","family":"Yuan","affiliation":"Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Huanjing Gao","slug":"huanjing-gao","given":"Huanjing","family":"Gao","affiliation":"School of Computer Engineering and Science , Shanghai University"}],"abstract":"Integrating data-driven and rule-based approaches is crucial for therapy recommendations since they can collaborate to achieve better performance. Medical rules, which are chains of reasoning that can infer therapies, widely exist. However, their symbolic and logical forms make integrating them with data-driven modeling technologies hard. Although rare attempts have indirectly modeled rules using data that supports them, the poor generalization of medical rules leads to inadequate supporting data and thus impairs the benefit of medical rules. To this end, we propose R2V-MIF, which fills the gap by rule-to-vector contrastive learning (R2V) and multi-channel information fusion (MIF). R2V is a data-free module and utilizes a hypergraph, including condition and result nodes, to instantiate the logic of medical rules. Each rule is reflected in the relations between nodes, and their representations are determined through contrastive learning. By taking rule representations as a bridge, MIF integrates the knowledge from medical rules, similar neighbors, and patient contents, and then recommends therapies. Extensive experiments show that R2V-MIF outperforms the baselines in several metrics using real-world medical data. Our code is available at https://github.com/vgeek-z/r2vmif.","doi":"10.24963/ijcai.2024/291","url":"https://ijcai.org/proceedings/2024/291","pdf":"https://ijcai.org/proceedings/2024/0291.pdf","markdown":"https://ijcai.org/proceedings/2024/291.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/291","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":291,"pages":{"first":2634,"last":2641},"keywords":["Data Mining: DM: Recommender systems","Data Mining: DM: Mining heterogenous data","Multidisciplinary Topics and Applications: MTA: Health and medicine"],"videos":[],"references":[{"position":1,"raw":"[Gradishar et al., 2018] William J Gradishar, Benjamin An- derson, Ron Balassanian, et al. 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