Title :
Hospital Admission Prediction Using Pre-hospital Variables
Author :
Li, Jiexun ; Guo, Lifan ; Handly, Neal
Author_Institution :
Coll. of Inf. Sci. Technol., Drexel Univ., Philadelphia, PA, USA
Abstract :
With the rapid outstripping of healthcare resources by the demands on hospital care, it is important to find more effective and efficient ways for managing care. This research is aimed at developing new admission prediction models using various pre-hospital variables to help hospital estimate the patients to be admitted. We developed a framework of hospital admission prediction and proposed two novel approaches to capture semantics of chief complaints to enhance prediction. Our experiments on a hospital dataset demonstrated that our proposed models outperformed several benchmark methods.
Keywords :
health care; medical administrative data processing; healthcare resources; hospital admission prediction model; hospital care; hospital dataset; pre-hospital variables; Abdomen; Carbon capture and storage; Demography; Educational institutions; Hospitals; Learning systems; Pain; Predictive models; Resource management; Standardization;
Conference_Titel :
Bioinformatics and Biomedicine, 2009. BIBM '09. IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-0-7695-3885-3
DOI :
10.1109/BIBM.2009.45