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Updated: Aug 9, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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使用无监督机器学习修复的Fallot四方论的现型聚类

Xander Jacquemyn1,2, Bhargava K Chinni1, Ashish N Doshi1

  • 1The Blalock-Taussig-Thomas Pediatric and Congenital Heart Center, Department of Pediatrics, Johns Hopkins School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, 1389 Blalock, Baltimore, 21287, MD, USA.

International journal of cardiology. Congenital heart disease
|December 23, 2024
PubMed
概括

经过修复的Fallot四重法 (rTOF) 患者可以使用机器学习将其分为四种不同的表型. 这种方法有助于理解疾病的异质性,并为更好地管理先天性心脏病患者量身定制干预措施.

关键词:
在CMR中,CMR是CMR.机器学习 机器学习现象类型的聚类 现象类型的聚类风险分层是指风险的分层.费洛特的四部曲 费洛特四部曲

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Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography

Published on: October 28, 2020

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Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

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相关概念视频

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...