{"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/interpretable-patient-mortality-prediction","title":"Interpretable Patient Mortality Prediction with Multi-value Rule Sets","arxiv_id":"1807.03633","date":"2018-07-06","proceeding":null,"authors":["Tong Wang","Veerajalandhar Allareddy","Sankeerth Rampa","Veerasathpurush Allareddy"],"abstract":"We propose a Multi-vAlue Rule Set (MRS) model for in-hospital predicting\npatient mortality. Compared to rule sets built from single-valued rules, MRS\nadopts a more generalized form of association rules that allows multiple values\nin a condition. Rules of this form are more concise than classical\nsingle-valued rules in capturing and describing patterns in data. Our\nformulation also pursues a higher efficiency of feature utilization, which\nreduces possible cost in data collection and storage. We propose a Bayesian\nframework for formulating a MRS model and propose an efficient inference method\nfor learning a maximum \\emph{a posteriori}, incorporating theoretically\ngrounded bounds to iteratively reduce the search space and improve the search\nefficiency. Experiments show that our model was able to achieve better\nperformance than baseline method including the current system used by the\nhospital.","url_abs":"http://arxiv.org/abs/1807.03633v2","url_pdf":"http://arxiv.org/pdf/1807.03633v2.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":"interpretable-patient-mortality-prediction","repo_url":"https://github.com/wangtongada/MARS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"form","task_name":"Form"},{"task_slug":"mortality-prediction","task_name":"Mortality Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}