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Updated: Sep 18, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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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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整合性机器学习方法用于预测在巨症中对第一代受体配体的抗性

Wei Lin1,2, Songchang Shi3, Yuanyuan Zheng1,4

  • 1Division of Endocrinology, Diabetes and hypertension, Brigham and Women's hospital, Harvard Medical School, Boston, MA, USA.

The Journal of clinical endocrinology and metabolism
|June 26, 2025
PubMed
概括

这项研究开发了一台机器学习计算器,用于预测壮症患者的治疗反应,帮助个性化治疗. 该工具准确地识别了可能从第一代体静止素受体配体 (fgSRLs) 中受益的个体.

关键词:
壮成长是一种壮成长.这是第一代索马托斯塔丁受体连接体.机器学习 机器学习个性化医疗是个性化的医疗.基于Web的临床计算器

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