{"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/revealing-patterns-in-hiv-viral-load-data-and","title":"Revealing patterns in HIV viral load data and classifying patients via a novel machine learning cluster summarization method","arxiv_id":"1804.11195","date":"2018-04-25","proceeding":null,"authors":["Samir Farooq","Samuel J. Weisenthal","Melissa Trayhan","Robert J. White","Kristen Bush","Peter R. Mariuz","Martin S. Zand"],"abstract":"HIV RNA viral load (VL) is an important outcome variable in studies of HIV\ninfected persons. There exists only a handful of methods which classify\npatients by viral load patterns. Most methods place limits on the use of viral\nload measurements, are often specific to a particular study design, and do not\naccount for complex, temporal variation. To address this issue, we propose a\nset of four unambiguous computable characteristics (features) of time-varying\nHIV viral load patterns, along with a novel centroid-based classification\nalgorithm, which we use to classify a population of 1,576 HIV positive clinic\npatients into one of five different viral load patterns (clusters) often found\nin the literature: durably suppressed viral load (DSVL), sustained low viral\nload (SLVL), sustained high viral load (SHVL), high viral load suppression\n(HVLS), and rebounding viral load (RVL). The centroid algorithm summarizes\nthese clusters in terms of their centroids and radii. We show that this allows\nnew viral load patterns to be assigned pattern membership based on the distance\nfrom the centroid relative to its radius, which we term radial normalization\nclassification. This method has the benefit of providing an objective and\nquantitative method to assign viral load pattern membership with a concise and\ninterpretable model that aids clinical decision making. This method also\nfacilitates meta-analyses by providing computably distinct HIV categories.\nFinally we propose that this novel centroid algorithm could also be useful in\nthe areas of cluster comparison for outcomes research and data reduction in\nmachine learning.","url_abs":"http://arxiv.org/abs/1804.11195v1","url_pdf":"http://arxiv.org/pdf/1804.11195v1.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":"revealing-patterns-in-hiv-viral-load-data-and","repo_url":"https://github.com/SamirRCHI/Viral_Load_Data_Categorization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"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}