个别机器学习算法在模拟当地医院小规模不平衡临床数据方面的潜能
Gang Li1, Chenbi Li1, Chengli Wang1
1Department of ICU, 3201 Hospital, Hanzhong, Shaanxi, China.
PloS one
|February 23, 2024
概括
基本的人工智能 (AI) 模型在生物化学分析的小型,不平衡的临床数据集中扎. 即使在调整后,性能也不足,这凸显了将AI应用于有限的,扭曲的数据中的挑战.
科学领域:
- 生物化学分析 生物化学分析
- 临床研究是临床研究.
- 人工智能 (AI) 应用程序
背景情况:
- 人工智能在生物化学分析等科学领域表现有前途.
- 人工智能在这些领域的小,不平衡的数据集上的有效性是不确定的.
- 多重黄蜂刺 (MWS) 呈现了一个临床情景,数据有限.
研究的目的:
- 在一个小,不平衡的临床数据集上评估八个基本的AI算法.
- 确定人工智能建模MWS患者生物化学血液检测记录的可行性.
- 在小规模,不平衡的生化数据中识别AI的挑战和潜在解决方案.
主要方法:
- 使用了来自MWS患者的一个小,不平衡的临床数据集 (n=387,类0=27,类1=360).
- 应用并评估了包括回归和分类模型在内的八种基本AI算法.
- 雇员 k 倍交叉验证和全面评分,用于严格的模型评估.
主要成果:
- 测试的AI模型中没有一个有效地模拟了小的,不平衡的生化数据集.
- 性能最好的模型的超参数调整没有产生可接受的结果.
- 所有评估的算法的性能指标仍然低于既定值.
结论:
- 当前的基本人工智能算法不适合对小规模,不平衡的生化或临床数据集进行建模.
- 需要专门为小规模数据设计的新型AI算法.
- 对转移学习,数据增强和最小数据集规模要求的进一步研究对于生物化学环境中的AI至关重要.
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