{"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/learning-compressed-representations-of-blood","title":"Learning compressed representations of blood samples time series with missing data","arxiv_id":"1710.07547","date":"2017-10-20","proceeding":null,"authors":["Filippo Maria Bianchi","Karl Øyvind Mikalsen","Robert Jenssen"],"abstract":"Clinical measurements collected over time are naturally represented as\nmultivariate time series (MTS), which often contain missing data. An\nautoencoder can learn low dimensional vectorial representations of MTS that\npreserve important data characteristics, but cannot deal explicitly with\nmissing data. In this work, we propose a new framework that combines an\nautoencoder with the Time series Cluster Kernel (TCK), a kernel that accounts\nfor missingness patterns in MTS. Via kernel alignment, we incorporate TCK in\nthe autoencoder to improve the learned representations in presence of missing\ndata. We consider a classification problem of MTS with missing values,\nrepresenting blood samples of patients with surgical site infection. With our\napproach, rather than with a standard autoencoder, we learn representations in\nlow dimensions that can be classified better.","url_abs":"http://arxiv.org/abs/1710.07547v1","url_pdf":"http://arxiv.org/pdf/1710.07547v1.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":"learning-compressed-representations-of-blood","repo_url":"https://github.com/FilippoMB/TCK_AE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}