Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage
Intracerebral and Subarachnoid Hemorrhage Research
Type
article
Field-Weighted Citation Impact
2.78
Funders
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province
Southern Medical University
Nanfang Hospital
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article
Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage
Qiheng Wu, Genggeng Qin, Jingjing Yang, Genghong Xia et al.
Citations
2 citations
Journal
BMC Medical Imaging
Topics
Intracerebral and Subarachnoid Hemorrhage Research
Field-Weighted Citation Impact (FWCI)
2.78
article
Clinical, radiological, and radiomics feature-based explainable machine learning models for prediction of neurological deterioration and 90-day outcomes in mild intracerebral hemorrhage