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Updated: Sep 17, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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临床实验室参数驱动的机器学习用于胃癌患者生物等价性研究的参与者选择:框架开发和验证研究

Byungeun Shon1, Sook Jin Seong2, Eun Jung Choi3

  • 1Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.

JMIR AI
|July 3, 2025
PubMed
概括

机器学习模型有效地使用实验室数据识别出符合条件的临床试验患者,显著减少招聘工作量并加速招生. 这种方法通过改善参与者选择来提高临床试验的效率.

关键词:
在这里,我们可以看到AIAIAI.韩国 韩国 韩国 韩国ML ML 在 ML人工智能的人工智能是人工智能.临床实验室试验 临床实验室试验临床试验临床试验临床试验临床试验临床试验电子医疗记录 电子医疗记录资格标准 资格标准一个基本的框架框架.胃癌 胃癌 是一种胃癌.机器学习是机器学习.模型开发模型的发展.参与者招生参加者招生患者招募 患者招募支持支持支持支持支持试验试验试验试验试验试验试验

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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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