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Updated: Jul 17, 2025

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

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

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IHCP:基于黑盒机器学习模型的可解释性型肝炎预测系统.

Yongxian Fan¹, Xiqian Lu², Guicong Sun²

¹School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China. yongxian.fan@gmail.com.

BMC bioinformatics

|September 6, 2023

概括

本研究介绍了一种可解释的机器学习模型,用于准确预测C型肝炎. 可解释的人工智能方法通过澄清早期诊断的决策过程来增强临床信任.

关键词:

在C型肝炎患者中,C型肝炎是最常见的.可解释的人工智能在 LIME 时代,机器学习是机器学习.这就是 SHAP SHAP 的意思.

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科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
肝病学肝病学是一种肝病学.

背景情况:

肝炎C是一种广泛的肝病,需要早期诊断才能有效治疗和预后.
目前用于C型肝炎预测的计算模型往往缺乏透明度,阻碍了临床采用.
在诊断C型肝炎时,急需可解释的医疗决策系统.

研究的目的:

开发和评估用于预测C型肝炎的机器学习 (ML) 模型.
通过可解释的人工智能技术来提高模型透明度.
通过可解释的预测过程,建立临床医生和患者的信任.

主要方法:

评估的黑盒模型:随机森林 (RF),支持矢量机 (SVM) 和AdaBoost.
利用贝叶斯优化的RF作为最终的分类算法.
采用了SHapley添加式解释 (SHAP) 来解释全球模型和局部可解释的模型-不可知解释 (LIME_stability) 来解释局部模型.

主要成果:

拟议的可解释的C型肝炎预测模型与最先进的方法相比,显示出更高的性能.
通过严格的五倍交叉验证和独立测试,实现了卓越的预测准确性.
成功为模型预测提供了全球和本地解释.

结论:

可解释的C型肝炎预测系统比现有方法具有显著的优势.
该模型实现了高预测性能,同时保持了出色的解释性.
对模型决策的更好理解促进了对肝炎C诊断的临床信任和采用.