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XGBoost-Based Simple Three-Item Model Accurately Predicts Outcomes of Acute Ischemic Stroke
Chen Chih Chung,
Emily Chia Yu Su
, Jia Hung Chen, Yi Tui Chen, Chao Yang Kuo
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此作品的通信作者
生物醫學資訊研究所
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引文 斯高帕斯(Scopus)
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Keyphrases
Outcome Prediction
100%
XGBoost
100%
Acute Ischemic Stroke
100%
Item Model
100%
Fasting Plasma Glucose
60%
National Institutes of Health Stroke Scale (NIHSS)
60%
XGBoost Model
40%
Acute Ischemic Stroke Treatment
40%
Clinical Evidence
20%
Medical Records
20%
Treatment Strategy
20%
Medical Center
20%
Prediction Accuracy
20%
Significant Predictors
20%
Functional Outcome
20%
Predictive Power
20%
Area under the Curve
20%
Clinical Decision-making
20%
Unfavorable Prognosis
20%
Extreme Gradient Boosting(XGBoost)
20%
Endovascular Therapy
20%
Ischemic Stroke Outcome
20%
All-inclusive
20%
Mathematics
Accurate Prediction
100%
Clinical Decision
100%
Predictive Power
100%
Treatment Strategy
100%
Medicine and Dentistry
Brain Ischemia
100%
National Institutes of Health Stroke Scale
42%
Medical Record
14%
Blood Glucose
14%
Clinical Decision Making
14%
Endovascular Surgery
14%
Neuroscience
Brain Ischemia
100%
Cerebrovascular Accident
42%
Clinical Decision Making
14%
Pharmacology, Toxicology and Pharmaceutical Science
Brain Ischemia
100%
Cerebrovascular Accident
42%