Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks
Bayesian networks; Blood pressure; Classification (of information); Creatinine; Failure (mechanical); Machine learning; Bayesia n networks; Comorbidities; Data discretization; Failure assessment; Machine-learning; Model dynamics; Organ failure; Renal failure; Sequential organ failure assessment scor...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-26418 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-264182023-05-29T17:10:16Z Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks Shah N.N.H. Razak A.A. Razak N.N. Ramasamy A.K. Abu-Samah A. Hasan M.S. 7401823793 56960052400 37059587300 16023154400 56719596600 54083209700 Bayesian networks; Blood pressure; Classification (of information); Creatinine; Failure (mechanical); Machine learning; Bayesia n networks; Comorbidities; Data discretization; Failure assessment; Machine-learning; Model dynamics; Organ failure; Renal failure; Sequential organ failure assessment score; Variables selections; Intensive care units Renal failure in the intensive care unit (ICU) is associated with high morbidity and mortality. The Sequential Organ Failure Assessment (SOFA) score is applied in the ICU to track the progression of organ dysfunction. The renal component of the SOFA score employed serum creatinine and urine output to define the stage of its dysfunction. This study aims to explore the relationship between commonly available variables in the ICU together patients' gender and comorbidities to renal failure employing Bayesian Network. The process of building Bayesian Networks involved variable selection, data discretization, and aggregation before structural learning method. The dataset was discretized using equal distance technique into 3 intervals before it was fed into unsupervised structural classification learning techniques. The highest overall precision of 85.1 % was achieved using the unsupervised learning Taboo Order Bayesian Network. Other than creatinine, heart rate, systolic blood pressure, temperature, diabetes mellitus, and hypertension are directly connected with renal failure in this Bayesian Network. � 2021 IEEE. Final 2023-05-29T09:10:16Z 2023-05-29T09:10:16Z 2021 Conference Paper 10.1109/ICSET53708.2021.9612523 2-s2.0-85123342305 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123342305&doi=10.1109%2fICSET53708.2021.9612523&partnerID=40&md5=7f62d948efe2eed2f626817ec4d247d0 https://irepository.uniten.edu.my/handle/123456789/26418 134 138 Institute of Electrical and Electronics Engineers Inc. Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
Bayesian networks; Blood pressure; Classification (of information); Creatinine; Failure (mechanical); Machine learning; Bayesia n networks; Comorbidities; Data discretization; Failure assessment; Machine-learning; Model dynamics; Organ failure; Renal failure; Sequential organ failure assessment score; Variables selections; Intensive care units |
| author2 |
7401823793 |
| author_facet |
7401823793 Shah N.N.H. Razak A.A. Razak N.N. Ramasamy A.K. Abu-Samah A. Hasan M.S. |
| format |
Conference Paper |
| author |
Shah N.N.H. Razak A.A. Razak N.N. Ramasamy A.K. Abu-Samah A. Hasan M.S. |
| spellingShingle |
Shah N.N.H. Razak A.A. Razak N.N. Ramasamy A.K. Abu-Samah A. Hasan M.S. Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks |
| author_sort |
Shah N.N.H. |
| title |
Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks |
| title_short |
Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks |
| title_full |
Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks |
| title_fullStr |
Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks |
| title_full_unstemmed |
Modeling Dynamic Patients Variables to Renal Failure in the Intensive Care Unit Using Bayesian Networks |
| title_sort |
modeling dynamic patients variables to renal failure in the intensive care unit using bayesian networks |
| publisher |
Institute of Electrical and Electronics Engineers Inc. |
| publishDate |
2023 |
| _version_ |
1806425803160813568 |
| score |
13.214268 |