Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network
Early treatment of sepsis can reduce mortality and improve a patient condition. However, the lack of clear information and accurate methods of diagnosing sepsis at an early stage makes it become a significant challenge. The decision to start, continue or stop antimicrobial therapy is normally base o...
| Main Authors: | , , , , |
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| Format: | Book Chapter |
| Language: | English |
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Springer
2016
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| Online Access: | http://umpir.ump.edu.my/id/eprint/11575/1/Prediction%20of%20Sepsis%20Progression%20in%20Critical%20Illness%20Using%20Artificial%20Neural%20Network.pdf |
| _version_ | 1796991011135684608 |
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| author | F. M., Suhaimi J. G., Chase G. M., Shaw Ummu Kulthum, Jamaludin Normy Norfiza, A. Razak |
| author2 | Fatimah, Ibrahim |
| author_facet | Fatimah, Ibrahim F. M., Suhaimi J. G., Chase G. M., Shaw Ummu Kulthum, Jamaludin Normy Norfiza, A. Razak |
| author_sort | F. M., Suhaimi |
| collection | UMP |
| description | Early treatment of sepsis can reduce mortality and improve a patient condition. However, the lack of clear information and accurate methods of diagnosing sepsis at an early stage makes it become a significant challenge. The decision to start, continue or stop antimicrobial therapy is normally base on clinical judgment since blood cultures will be negative in the majority of cases of septic shock or sepsis. However, clinical guidelines are still required to provide guidance for the clinician caring for a patient with severe sepsis or septic shock. Guidelines based on patient’s unique set of clinical variables will help a clinician in the process of decision making of suitable treatment for the particular patient. Therefore, biomarkers for sepsis diagnosis with a reasonable sensitivity and specificity are a requirement in ICU settings, as a guideline for the treatment. Moreover, the biomarker should also allow availability in real-time and prediction of sepsis progression to avoid delay in treatment and worsen the patient condition. |
| first_indexed | 2024-03-06T12:00:00Z |
| format | Book Chapter |
| id | UMPir11575 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:00:00Z |
| publishDate | 2016 |
| publisher | Springer |
| record_format | dspace |
| spelling | UMPir115752019-07-17T02:52:21Z http://umpir.ump.edu.my/id/eprint/11575/ Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network F. M., Suhaimi J. G., Chase G. M., Shaw Ummu Kulthum, Jamaludin Normy Norfiza, A. Razak TJ Mechanical engineering and machinery Early treatment of sepsis can reduce mortality and improve a patient condition. However, the lack of clear information and accurate methods of diagnosing sepsis at an early stage makes it become a significant challenge. The decision to start, continue or stop antimicrobial therapy is normally base on clinical judgment since blood cultures will be negative in the majority of cases of septic shock or sepsis. However, clinical guidelines are still required to provide guidance for the clinician caring for a patient with severe sepsis or septic shock. Guidelines based on patient’s unique set of clinical variables will help a clinician in the process of decision making of suitable treatment for the particular patient. Therefore, biomarkers for sepsis diagnosis with a reasonable sensitivity and specificity are a requirement in ICU settings, as a guideline for the treatment. Moreover, the biomarker should also allow availability in real-time and prediction of sepsis progression to avoid delay in treatment and worsen the patient condition. Springer Fatimah, Ibrahim Juliana, Usman Mas Sahidayana, Mokhtar Mohd Yazed, Ahmad 2016 Book Chapter PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/11575/1/Prediction%20of%20Sepsis%20Progression%20in%20Critical%20Illness%20Using%20Artificial%20Neural%20Network.pdf F. M., Suhaimi and J. G., Chase and G. M., Shaw and Ummu Kulthum, Jamaludin and Normy Norfiza, A. Razak (2016) Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network. In: International Conference for Innovation in Biomedical Engineering and Life Sciences. IFMBE Proceedings, 56 . Springer, Singapore, pp. 127-132. ISBN 978-981-10-0265-6 (print); 978-981-10-0266-3 (online) http://dx.doi.org/10.1007/978-981-10-0266-3_26 DOI: 10.1007/978-981-10-0266-3_26 |
| spellingShingle | TJ Mechanical engineering and machinery F. M., Suhaimi J. G., Chase G. M., Shaw Ummu Kulthum, Jamaludin Normy Norfiza, A. Razak Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network |
| title | Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network |
| title_full | Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network |
| title_fullStr | Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network |
| title_full_unstemmed | Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network |
| title_short | Prediction of Sepsis Progression in Critical Illness Using Artificial Neural Network |
| title_sort | prediction of sepsis progression in critical illness using artificial neural network |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/11575/1/Prediction%20of%20Sepsis%20Progression%20in%20Critical%20Illness%20Using%20Artificial%20Neural%20Network.pdf |
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