{"type":"ScholarlyArticle","title":"ContextCare: Incorporating Contextual Information Networks to Representation Learning on Medical Forum Data","authors":[{"name":"Stan Zhao","slug":"stan-zhao","given":"Stan","family":"Zhao","affiliation":"* Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Meng Jiang","slug":"meng-jiang","given":"Meng","family":"Jiang","affiliation":""},{"name":"Quan Yuan","slug":"quan-yuan","given":"Quan","family":"Yuan","affiliation":"‡ Department of Computer Science , University of Illinois at Urbana-Champaign , USA"},{"name":"Bing Qin","slug":"bing-qin","given":"Bing","family":"Qin","affiliation":"* Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":"* Research Center for Social Computing and Information Retrieval , Harbin Institute of Technology , China"},{"name":"ChengXiang Zhai","slug":"chengxiang-zhai","given":"ChengXiang","family":"Zhai","affiliation":"‡ Department of Computer Science , University of Illinois at Urbana-Champaign , USA"}],"abstract":"Online users have generated a large amount of health-related data on medical forums and search engines. However, exploiting these rich data for orienting patient online and assisting medical checkup offline is nontrivial due to the sparseness of existing symptom-disease links, which caused by the natural and chatty expressions of symptoms. In this paper, we propose a novel and general representation learning method ContextCare for human generated health-related data, which learns the latent relationship between symptoms and diseases from the symptom-disease diagnosis network for disease prediction, disease category prediction and disease clustering. To alleviate the network sparseness, ContextCare adopts regularizations from rich contextual information networks including a symptom co-occurrence network and a disease evolution network. Therefore, our representations of symptoms and diseases incorporate knowledge from these three networks. Extensive experiments on medical forum data demonstrate that ContextCare outperforms the state-of-the-art methods in disease category prediction, disease prediction and disease clustering.","doi":"10.24963/ijcai.2017/489","url":"https://ijcai.org/proceedings/2017/489","pdf":"https://ijcai.org/proceedings/2017/0489.pdf","markdown":"https://ijcai.org/proceedings/2017/489.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/489","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":489,"pages":{"first":3497,"last":3503},"keywords":["Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Adamic and Adar, 2003] Lada A Adamic and Eytan Adar. 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