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Prediction of blood pressure variability using deep neural networks
Hiroshi Koshimizu
, Ryosuke Kojima
,
Kazuomi Kario
, Yasushi Okuno
Research output
:
Contribution to journal
›
Article
›
peer-review
65
Scopus citations
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Keyphrases
Blood Pressure Variability
100%
Deep Neural Network
100%
Blood Pressure
80%
Prediction Model
40%
High Variability
40%
Time Series Data
40%
Risk Factors for Cardiovascular Diseases
20%
Independent Risk Factors
20%
Pressure Value
20%
Medical Examination
20%
Evaluation Metrics
20%
Evaluation Method
20%
Root Mean Square Error
20%
Performance Prediction
20%
Variability in Blood Pressure
20%
Blood Pressure Management
20%
Multi-output
20%
Standard Deviation Ratio
20%
Multi-input multi-output
20%
Medical Examination Data
20%
Engineering
Mean Value
100%
Data Series
100%
Deep Neural Network
100%
Metrics
50%
Prediction Performance
50%
Root Mean Square Error
50%
Pressure Management
50%
Multi-Input Multi-Output
50%
Neuroscience
Neural Network
100%
Prediction Model
100%
Risk Factor
50%