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Updated: Jun 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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SymScore:机器学习准确度在基于符号回归的临床分数生成器中满足透明度

Olive R Cawiding1, Sieun Lee2, Hyeontae Jo3

  • 1Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.

Computers in biology and medicine
|December 25, 2024
PubMed
概括

本研究介绍了SymScore,这是一种工具,可以为缩短的临床调查问卷创建可解释的得分表. 在不需要机器学习专业知识的情况下,SymScore提供了准确的疾病风险评估,提高了临床信任和效率.

关键词:
临床决策 临床决策可解释的人工智能可以解释的机器学习医疗问卷 医学问卷风险评分评估 风险评分评估缩短了问卷调查的时间.象征性回归是一种象征性回归.

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