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  • Regression forecasting of patient admission data

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    58567_1.pdf (273.0Kb)
    Author(s)
    Boyle, Justin
    Wallis, Marianne
    Jessup, Melanie
    Crilly, Julia
    Lind, James
    Miller, Peter
    Fitzgerald, Gerard
    Griffith University Author(s)
    Crilly, Julia
    Year published
    2008
    Metadata
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    Abstract
    Forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. This paper describes our work in analyzing patient admission data and forecasting this data using regression techniques. Five years of Emergency Department admissions data were obtained from two hospitals with different demographic techniques. Forecasts made from regression models were compared with observed admission data over a 6-month horizon. The best method was linear regression using 11 dummy variables to model monthly variation (MAPE=1.79%). Similar performance was achieved ...
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    Forecasting is an important aid in many areas of hospital management, including elective surgery scheduling, bed management, and staff resourcing. This paper describes our work in analyzing patient admission data and forecasting this data using regression techniques. Five years of Emergency Department admissions data were obtained from two hospitals with different demographic techniques. Forecasts made from regression models were compared with observed admission data over a 6-month horizon. The best method was linear regression using 11 dummy variables to model monthly variation (MAPE=1.79%). Similar performance was achieved with a 2-year average, supporting further investigation at finer time scales.
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    Conference Title
    2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8
    Volume
    2008
    DOI
    https://doi.org/10.1109/IEMBS.2008.4650041
    Copyright Statement
    © 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Subject
    Multi-Disciplinary
    Publication URI
    http://hdl.handle.net/10072/28695
    Collection
    • Conference outputs

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