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Source: Adv Data
Condition: Hemorrhagic Stroke
Education: Learning
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Total 4 results found since Jan 2013.
Machine learning prediction of symptomatic intracerebral hemorrhage after stroke thrombolysis: a cross-cultural validation in Caucasian and Han Chinese cohort
CONCLUSION: The established SVM model is feasible for predicting the risk of sICH after thrombolysis quickly and efficiently in both Caucasian and Han Chinese cohort.PMID:36225969 | PMC:PMC9549180 | DOI:10.1177/17562864221129380
Source: Adv Data - October 13, 2022 Category: Epidemiology Authors: Junfeng Liu Xinyue Chen Xiaonan Guo Renjie Xu Yanan Wang Ming Liu Source Type: research
Radiomics-based prediction of hemorrhage expansion among patients with thrombolysis/thrombectomy related-hemorrhagic transformation using machine learning
CONCLUSIONS: The currently established NECT-based radiomic score is valuable in predicting hemorrhage expansion after HT among patients treated with reperfusion treatment after ischemic stroke, which may aid clinicians in determining patients with HT who are most likely to benefit from anti-expansion treatment.PMID:35173809 | PMC:PMC8842178 | DOI:10.1177/17562864211060029
Source: Adv Data - February 17, 2022 Category: Epidemiology Authors: Junfeng Liu Wendan Tao Zhetao Wang Xinyue Chen Bo Wu Ming Liu Source Type: research
