• Login
    • Library Home
    View Item 
    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
    •   BracU IR
    • School of Data and Sciences (SDS)
    • Department of Computer Science and Engineering (CSE)
    • Thesis & Report, BSc (Computer Science and Engineering)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data

    Thumbnail
    View/Open
    17141041,17341006_CSE.pdf (754.0Kb)
    Date
    2017-08
    Publisher
    BRAC Univeristy
    Author
    Mazhar, Tabib Ibne
    Suha, Nusrat Jahan
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/10361/9074
    Abstract
    In order to take better care and to ensure better facilities to the inpatients, predicting length of stay serves great importance. Since, the resources and the doctors are limited in the hospital especially in a developing countries like Bangladesh, it is quite difficult to provide proper healthcare to the inpatients. Not only because of limited hospital resources but also, it is difficult for the inpatients to bear the expense for a long period as well. In addition to that, if doctors can predict length of stay at the early stage of preadmission, they can map a well instructed way for example, which treatment, which instrument will treat patient best. As a result the patient can start his treatment with a slight assumption of the expenses. If we can predict accurate length of stay, patients do not have to leave in between the treatment without medical advice. Keeping all this point in mind, we decided to developed a study using machine learning algorithm and artificial neural network (ANN) to predict length of stay for Spinal Cord Injured (SCI) patients. For this purpose we chose Centre for the Rehabilitation of the Paralysed (CRP). They provided us around 500 inpatients data who has been released from the hospital after completing their treatment.
    Keywords
    SCI; LOS; Admission; Neural network
     
    Description
    This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
     
    Cataloged from PDF version of thesis report.
     
    Includes bibliographical references (pages 34-35).
    Department
    Department of Computer Science and Engineering, BRAC University
    Collections
    • Thesis & Report, BSc (Computer Science and Engineering)

    Copyright © 2008-2019 Ayesha Abed Library, Brac University 
    Contact Us | Send Feedback
     

     

    Policy Guidelines

    • BracU Policy
    • Publisher Policy

    Browse

    All of BracU Institutional RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Statistics

    View Usage Statistics

    Copyright © 2008-2019 Ayesha Abed Library, Brac University 
    Contact Us | Send Feedback