利用 Snorkel Flow 扩大 MSKCC 的临床试验筛选规模
技术
- 传感器 - 流量计
- 传感器 - 液体检测传感器
适用行业
- 水泥
- 教育
适用功能
- 产品研发
- 质量保证
用例
- 篡改检测
- 虚拟培训
服务
- 测试与认证
- 培训
关于客户
纪念斯隆凯特琳癌症中心 (MSKCC) 是世界上历史最悠久、规模最大的私立癌症中心。它每年为超过 150,000 名癌症患者提供护理，以提高他们的生活质量。为此，他们利用人工智能来加速发现更有效的策略，以预防、控制和最终治愈未来的癌症。 MSKCC 数据科学团队面临的挑战是使用 AI/ML 根据 HER-2（一种许多癌症常见的蛋白质）的存在对患者记录进行分类。
挑战
纪念斯隆凯特琳癌症中心 (MSKCC) 是世界上历史最悠久、规模最大的癌症中心，它面临着通过对相关蛋白质 HER-2 的存在进行分类来确定患者作为临床试验研究候选者的挑战。审查 HER-2 患者记录的过程既费力又耗时，因为它需要临床医生和研究人员筛选复杂、多变的患者数据。 MSKCC 的数据科学团队希望使用 AI/ML 根据 HER-2 的存在对患者记录进行分类，但缺乏标记的训练数据是一个重大瓶颈。标记数据，尤其是复杂的患者记录，需要临床医生和研究人员的专业知识，而且速度缓慢且昂贵。即使专家能够手动注释训练数据，他们的标签有时也会不一致，从而限制了模型的性能潜力。
解决方案
MSKCC 使用 Snorkel Flow 构建了一个 AI 应用程序，将患者记录分为五个类别，对 HER-2 的存在进行分类。该应用程序用于下游临床试验筛选系统，以识别潜在的临床试验参与者。该团队使用了他们之前在平台外部标记的 3,200 个数据点。他们提取数据并将其划分为训练集、验证集和测试集。开发该项目的首席生物信息学工程师只编写了八个嘈杂、不完美的标记函数，Snorkel Flow 将这些函数组合起来自动标记训练数据集。他们用它来训练平台内的 XGboost 模型。使用平台内的错误分析工具，团队利用该模型的反馈来了解其中的混淆之处以及如何纠正。经过几次快速迭代后，团队在所有类别中实现了 93% 的总体准确率和 87% 的平均 F1。
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