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Evolving classification of intensive care patients from event data
Mark Last
, Olga Tosas
, Tiziano Gallo Cassarino
, Zisis Kozlakidis
, Jonathan Edgeworth
Research output
:
Contribution to journal
›
Article
›
peer-review
9
Scopus citations
Overview
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Dive into the research topics of 'Evolving classification of intensive care patients from event data'. Together they form a unique fingerprint.
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Keyphrases
Accurate Model
14%
Admission Day
14%
Adult Patients
28%
Area under the Curve
85%
Classification Algorithms
14%
Classification Approach
14%
Classification Model
14%
Decision Tree Algorithm
14%
Decision Tree Model
14%
Development Training
14%
Discharge Outcomes
14%
Event Classification
14%
Event Data
100%
Evolving Classification
100%
Hospitalization Days
14%
Incremental Information
57%
Information Networks
100%
Intensive Care Patients
100%
Intensive Care Unit
57%
Intensive Care Unit Admission
14%
J48 Decision Tree
14%
Logistic Regression
14%
Network Algorithms
28%
Network Area
42%
Patient Discharge
14%
Patient Monitoring
14%
Prediction Accuracy
14%
Predictive Features
14%
Random Partition
14%
Read-once
14%
Regenerative Approach
14%
Selected Features
14%
St. Thomas
14%
Temporal Order
28%
Training Set
14%
United Kingdom
14%
Validation Set
14%
Nursing and Health Professions
Area under the Curve
100%
Classification Algorithm
16%
Decision Trees
50%
Hospital Discharge
16%
Intensive Care
100%
Intensive Care Unit
83%
Logistic Regression Analysis
16%
Patient Monitoring
16%
Step Time
33%
Computer Science
Classification Algorithm
14%
classification approach
14%
Classification Models
14%
Data Stream
14%
Decision Tree Algorithm
28%
Decision Tree Model
14%
Experimental Result
14%
Information Network
100%
Logistic Regression
14%
Network Algorithm
28%
Predictive Accuracy
14%
Training Development
14%
Validation Set
14%
Agricultural and Biological Sciences
Patient Care
100%
Retrospective Study
50%
Pharmacology, Toxicology and Pharmaceutical Science
Retrospective Study
33%
Tree
100%
Psychology
Training Set
100%