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Predicting the prolonged length of stay of general surgery patients: a supervised learning approach
Mao Te Chuang
,
Ya Han Hu
, Chia Lun Lo
資訊管理學系
研究成果
:
雜誌貢獻
›
期刊論文
›
同行評審
37
引文 斯高帕斯（Scopus）
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深入研究「Predicting the prolonged length of stay of general surgery patients: a supervised learning approach」主題。共同形成了獨特的指紋。
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Keyphrases
Supervised Learning Method
100%
Surgery Patients
100%
Prolonged Length of Stay
100%
General Surgery
100%
Urgent Operation
80%
Prediction Model
60%
Comorbidity
40%
Influential Variables
40%
Non-urgent
40%
After Surgery
20%
Making Decisions
20%
Prediction Accuracy
20%
Medical Records
20%
Blood Pressure
20%
Predictive Models
20%
Body Temperature
20%
Resource Management
20%
Random Forest Method
20%
Before Surgery
20%
Blood Sugar
20%
Medical Resources
20%
Length of Stay
20%
Gain Ratio
20%
Clinical Decision Support Systems
20%
Clinical Care
20%
Patient Safety
20%
Ratio Technique
20%
Lab Data
20%
Medical Record Data
20%
Length of Stay Prediction
20%
Stable Prediction
20%
Patient Length of Stay
20%
Blood Transfusion
20%
Communicating with Patients
20%
ICU Admission
20%
Plasma Creatinine
20%
Medicine and Dentistry
Blood Transfusion
100%
Medical Record
100%
General Surgery
100%
Comorbidity
100%
Decision Making
50%
Blood Pressure
50%
Clinical Decision Support System
50%
Patient Safety
50%
Creatinine
50%
Biochemistry, Genetics and Molecular Biology
Medical Record
100%
Blood Transfusion
100%
Comorbidity
100%
Blood Pressure
50%
Random Forest
50%
Decision Making
50%
Body Temperature
50%
Creatinine
50%
Clinical Decision Support System
50%
Computer Science
Prediction Model
100%
Supervised Learning
100%
Learning Approach
100%
Medical Record
66%
Learning Technique
66%
Decision-Making
33%
Random Decision Forest
33%
Predictive Model
33%
Clinical Decision Support System
33%
Body Temperature
33%
Pharmacology, Toxicology and Pharmaceutical Science
Comorbidity
100%
Creatinine
50%