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  • An incremental EM-based learning approach for on-line prediction of hospital resource utilization

    Author(s)
    Ng, SK
    McLachlan, GJ
    Lee, AH
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2006
    Metadata
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    Abstract
    Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batch-mode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information ...
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    Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batch-mode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data.
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    Journal Title
    Artificial Intelligence in Medicine
    Volume
    36
    Issue
    3
    Publisher URI
    http://www.elsevier.com/wps/find/journaldescription.cws_home/505627/description#description
    DOI
    https://doi.org/10.1016/j.artmed.2005.07.003
    Subject
    Information and computing sciences
    Engineering
    Publication URI
    http://hdl.handle.net/10072/20783
    Collection
    • Journal articles

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