图形和表格深度学习模型的融合用于预测慢性病
Patike Kiran Rao1, Subarna Chatterjee1, K Nagaraju2
1Department of Computer Science and Engineering, Faculty of Engineering, MS Ramaiah University of Applied Sciences, Bengaluru 560054, Karnataka, India.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
这项研究引入了一种新的深度学习模型,将图形神经网络 (GNN) 和表格数据结合起来,以预测慢性病 (CKD) 的进展. 融合模型显著提高了预测准确性,有助于早期干预.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 腎臟病學 (nephrology) 是一種醫學.
背景情况:
- 慢性病 (CKD) 构成了全球健康的重大负担.
- 准确预测CKD进展对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和评估一种创新的融合深度学习模型,用于预测CKD进展.
- 为了提高预测能力,利用图形结构和表格数据.
主要方法:
- 开发了一个融合深度学习模型,集成了一个图形神经网络 (GNN) 和一个表格数据模型.
- 融合模型的性能与独立的GNN,表格模型和使用精度,精度,回忆和F1分数的基线进行了比较.
- 使用网格搜索进行了超参数优化,以进行可靠的模型比较.
主要成果:
- 与单个模型和基线相比,融合模型在所有评估指标上表现出卓越的表现.
- 该GNN组件对增强的预测能力做出了重大贡献,突出了图形结构数据的价值.
- 融合模型在各种数据分割中显示出一致的性能,表明对数据集变化的稳定性.
结论:
- 拟议的融合深度学习模型为预测CKD进展提供了更精确,更可靠的方法.
- 这种方法有可能推进精准医学,改善慢性脏病的早期检测和治疗.
- 整合图形和表格数据可以提高对诸如CKD之类的复杂疾病的预测准确度.
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