Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data
This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017.
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| Format: | Praca dyplomowa |
| Język: | English |
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BRAC Univeristy
2018
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| Dostęp online: | http://hdl.handle.net/10361/9074 |
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10361-9074 |
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10361-90742022-01-26T10:21:41Z Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data Mazhar, Tabib Ibne Suha, Nusrat Jahan Ali, Dr. Md. Haider Department of Computer Science and Engineering, BRAC University SCI LOS Admission Neural network 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). 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. Tabib Ibne Mazhar Nusrat Jahan Suha B. Computer Science and Engineering 2018-01-15T10:15:36Z 2018-01-15T10:15:36Z 2017 2017-08 Thesis ID 17141041 ID 17341006 http://hdl.handle.net/10361/9074 en 35 pages application/pdf BRAC Univeristy |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
SCI LOS Admission Neural network |
| spellingShingle |
SCI LOS Admission Neural network Mazhar, Tabib Ibne Suha, Nusrat Jahan Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data |
| 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. |
| author2 |
Ali, Dr. Md. Haider |
| author_facet |
Ali, Dr. Md. Haider Mazhar, Tabib Ibne Suha, Nusrat Jahan |
| format |
Thesis |
| author |
Mazhar, Tabib Ibne Suha, Nusrat Jahan |
| author_sort |
Mazhar, Tabib Ibne |
| title |
Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data |
| title_short |
Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data |
| title_full |
Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data |
| title_fullStr |
Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data |
| title_full_unstemmed |
Spinal Cord Injured (SCI) patients Length of Stay (LOS) prediction based on admission data |
| title_sort |
spinal cord injured (sci) patients length of stay (los) prediction based on admission data |
| publisher |
BRAC Univeristy |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/9074 |
| work_keys_str_mv |
AT mazhartabibibne spinalcordinjuredscipatientslengthofstaylospredictionbasedonadmissiondata AT suhanusratjahan spinalcordinjuredscipatientslengthofstaylospredictionbasedonadmissiondata |
| _version_ |
1814309340882927616 |