基于临床数据的个性化抗瘤药物疗效预测
Xinping Xie1, Dandan Li1, Yangyang Pei1
1School of Mathematics and Physics, Anhui Jianzhu University, Hefei, China.
Heliyon
|March 19, 2024
概括
这项研究使用临床数据和潜在迪里克莱特分配 (LDA) 主题建模预测抗瘤药物疗效. 该方法有效地分类患者对肺癌和肠道癌症的基化疗的反应.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 个性化医疗需要准确预测抗瘤药物的疗效.
- 临床数据为预测药物反应提供了丰富但未得到充分利用的资源.
- 现有的方法往往难以有效利用复杂的临床文本数据.
研究的目的:
- 利用临床文本数据开发一种用于预测个性化抗瘤药物疗效的新方法.
- 为了将癌症患者分类为对特定药物的反应或不反应.
- 证明隐性迪里克莱特分配 (LDA) 在药物疗效预测中代表临床文本的价值.
主要方法:
- 收集了用治疗的肺癌和肠癌患者的临床文本数据.
- 隐性迪里克莱特分配 (LDA) 用于将临床文本编码为主题表示的数量向量.
- 机器学习模型在LDA主题表示上受过训练,以预测药物疗效.
主要成果:
- 拟议的方法在预测患者对基化疗的反应方面表现出显著的有效性和有效性.
- 实验结果验证了对现实世界临床数据集的方法.
- 这项研究强调了临床数据在推进癌症精准医学方面的潜力.
结论:
- 从临床数据中预测抗瘤药物的疗效是可行的,对个性化医学有价值的.
- LDA主题建模提供了一种有效的方式来表示和利用临床文本进行预测建模.
- 这种方法有望改善癌症治疗策略和患者的治疗结果.
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