{"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/using-data-mining-to-predict-hospital","title":"Using Data Mining to Predict Hospital Admissions From the Emergency Department","arxiv_id":null,"date":"2018-02-22","proceeding":"journal 2018 2","authors":["BYRON GRAHAM 1","RAYMOND BOND2","MICHAEL QUINN3","AND MAURICE MULVENNA2","(Senior Member","IEEE)"],"abstract":"Crowding within emergency departments (EDs) can have signi\u001ccant negative consequences\r\nfor patients. EDs therefore need to explore the use of innovative methods to improve patient \u001dow and prevent\r\novercrowding. One potential method is the use of data mining using machine learning techniques to predict\r\nED admissions. This paper uses routinely collected administrative data (120 600 records) from two major\r\nacute hospitals in Northern Ireland to compare contrasting machine learning algorithms in predicting the risk\r\nof admission from the ED. We use three algorithms to build the predictive models: 1) logistic regression;\r\n2) decision trees; and 3) gradient boosted machines (GBM). The GBM performed better (accuracy D\r\n80:31%, AUC-ROC D 0:859) than the decision tree (accuracy D 80:06%, AUC-ROC D 0:824) and the\r\nlogistic regression model (accuracy D 79:94%, AUC-ROC D 0:849). Drawing on logistic regression,\r\nwe identify several factors related to hospital admissions, including hospital site, age, arrival mode, triage\r\ncategory, care group, previous admission in the past month, and previous admission in the past year. This\r\npaper highlights the potential utility of three common machine learning algorithms in predicting patient\r\nadmissions. Practical implementation of the models developed in this paper in decision support tools\r\nwould provide a snapshot of predicted admissions from the ED at a given time, allowing for advance\r\nresource planning and the avoidance bottlenecks in patient \u001dow, as well as comparison of predicted and\r\nactual admission rates. When interpretability is a key consideration, EDs should consider adopting logistic\r\nregression models, although GBM's will be useful where accuracy is paramount.","url_abs":"https://ieeexplore.ieee.org/document/8300528","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8300528","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":"using-data-mining-to-predict-hospital","repo_url":"https://github.com/robertvazan/sourceafis-java","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}