{"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/unsupervised-learning-for-computational","title":"Unsupervised Learning for Computational Phenotyping","arxiv_id":"1612.08425","date":"2016-12-26","proceeding":null,"authors":["Chris Hodapp"],"abstract":"With large volumes of health care data comes the research area of\ncomputational phenotyping, making use of techniques such as machine learning to\ndescribe illnesses and other clinical concepts from the data itself. The\n\"traditional\" approach of using supervised learning relies on a domain expert,\nand has two main limitations: requiring skilled humans to supply correct labels\nlimits its scalability and accuracy, and relying on existing clinical\ndescriptions limits the sorts of patterns that can be found. For instance, it\nmay fail to acknowledge that a disease treated as a single condition may really\nhave several subtypes with different phenotypes, as seems to be the case with\nasthma and heart disease. Some recent papers cite successes instead using\nunsupervised learning. This shows great potential for finding patterns in\nElectronic Health Records that would otherwise be hidden and that can lead to\ngreater understanding of conditions and treatments. This work implements a\nmethod derived strongly from Lasko et al., but implements it in Apache Spark\nand Python and generalizes it to laboratory time-series data in MIMIC-III. It\nis released as an open-source tool for exploration, analysis, and\nvisualization, available at https://github.com/Hodapp87/mimic3_phenotyping","url_abs":"http://arxiv.org/abs/1612.08425v2","url_pdf":"http://arxiv.org/pdf/1612.08425v2.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":"unsupervised-learning-for-computational","repo_url":"https://github.com/Hodapp87/mimic3_phenotyping","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"computational-phenotyping","task_name":"Computational Phenotyping"},{"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}