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Deep Learning for Intradialytic Hypotension Prediction in Hemodialysis Patients
- Source :
- IEEE Access, Vol 8, Pp 82382-82390 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Intradialytic hypotension is a common problem during hemodialysis treatment. Despite several clinical variables have been authenticated for associations during dialysis session, the interaction effects between variables has not yet been presented. Our study aimed to investigate clinical factors associated with intradialytic hypotension by deep learning. A total of 279 participants with 780 hemodialysis sessions on an outpatient in a hospital-facilitated hemodialysis center were enrolled in March 2018. Associations between clinical factors and intradialytic hypotension were determined using linear regression method and deep neural network. A full-adjusted model indicated that intradialytic hypotension is positively associated with body mass index (Beta = 0.17, p = 0.028), hypertension comorbidity (Beta = 0.17, p = 0.008), and ultrafiltration amount (Beta = 0.31, p
- Subjects :
- medicine.medical_specialty
Clinical variables
General Computer Science
medicine.medical_treatment
02 engineering and technology
01 natural sciences
Internal medicine
Linear regression
0202 electrical engineering, electronic engineering, information engineering
Medicine
General Materials Science
Dialysis
business.industry
010401 analytical chemistry
intradialytic hypotension
General Engineering
deep learning
020206 networking & telecommunications
medicine.disease
Comorbidity
Predictive value
0104 chemical sciences
Hemodialysis
Cardiology
lcsh:Electrical engineering. Electronics. Nuclear engineering
Intradialytic hypotension
business
lcsh:TK1-9971
Body mass index
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....f6ca6cc22dc321f2f41d5388529a27a5