生物-K-变压器:一种预训练过的基于变压器的序列对序列模型,用于预测药物不良反应
Xihe Qiu1, Siyue Shao1, Haoyu Wang1
1School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.
Computer methods and programs in biomedicine
|December 12, 2024
概括
准确预测药物不良反应 (ADR) 对患者安全至关重要. 一个新的模型,Bio-K-Transformer,在市场上市之前预测ADR的准确率达到90.08%,超过现有的方法.
科学领域:
- 药物监督 药物监督 药物监督
- 计算化学的计算化学
- 生物医学信息学 生物医学信息学
背景情况:
- 药物不良反应 (ADRs) 存在重大患者安全风险,包括死亡率.
- 目前的临床试验和自愿报告等方法在检测所有ADR方面存在局限性.
- 对于改善市场前不良反应预测来减轻与药物相关的危害,这是非常需要的.
研究的目的:
- 在药物上市之前开发一种新的,准确的模型来预测药物不良反应 (ADRs).
- 加强早期识别潜在的药物安全问题.
- 改善药品的整体安全评估过程.
主要方法:
- 使用Bio-K-Transformer模型将ADR预测作为一个序列对序列问题.
- 集成的变压器架构与预训练的Bio_ClinicalBERT和K-bert模型.
- 增强了注意力机制和嵌入层,利用对目标数据的掩盖技术.
主要成果:
- 对于潜在的药物不良反应,达到90.08%的预测准确度.
- 与最先进的基线模型相比,显著提高了性能,包括先进的Llama变体.
- 在识别ADR方面展示了高精度,灵敏度和特异性.
结论:
- 生物-K-变压器模型大大提高了ADR预测的准确性.
- 介绍了一种具有成本效益的解决方案,用于增强市场前药物安全性评估.
- 为主动药物安全管理提供了一个有前途的工具.
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