{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/representation-learning-for-medical-data","title":"Representation Learning for Medical Data","arxiv_id":"2001.08269","date":"2020-01-22","proceeding":null,"authors":["Karol Antczak"],"abstract":"We propose a representation learning framework for medical diagnosis domain. It is based on heterogeneous network-based model of diagnostic data as well as modified metapath2vec algorithm for learning latent node representation. We compare the proposed algorithm with other representation learning methods in two practical case studies: symptom/disease classification and disease prediction. We observe a significant performance boost in these task resulting from learning representations of domain data in a form of heterogeneous network.","url_abs":"https://arxiv.org/abs/2001.08269v1","url_pdf":"https://arxiv.org/pdf/2001.08269v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"representation-learning-for-medical-data","repo_url":"https://github.com/KarolAntczak/multimetapath2vec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"diagnostic","task_name":"Diagnostic"},{"task_slug":"disease-prediction","task_name":"Disease Prediction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"medical-diagnosis","task_name":"Medical Diagnosis"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"metapath2vec","method_name":"metapath2vec"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}