UCSF-BASIC 使用 DataRobot 和手术室数据来预测创伤性脊髓损伤患者的预后
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- DataRobot
技术栈
- Predictive Analytics
- Automated Feature Discovery
实施规模
- Departmental Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
技术
- 分析与建模 - 预测分析
适用行业
- 医疗保健和医院
适用功能
- 产品研发
用例
- 预测性维护
- 远程病人监护
服务
- 数据科学服务
关于客户
本案例研究中的客户是加州大学旧金山分校的脊髓损伤研究和临床知识转化 (TRACK-SCI) 团队。这个由研究人员和临床医生组成的跨学科团队是加州大学旧金山分校脑和脊髓损伤中心的一部分。他们的使命是开展高质量的研究，研究当前的临床实践并通过与临床医生的合作改善护理。他们特别感兴趣的是手术过程中的血压管理如何影响患者从脊髓损伤中恢复的可能性。
挑战
加州大学旧金山分校的脊髓损伤研究和临床知识转化 (TRACK-SCI) 团队致力于改善创伤性脊髓损伤患者的护理。每年，美国有 17,000 例脊髓损伤 (SCI) 病例，通常会导致瘫痪和感觉功能障碍等永久性问题。每位患者的终生费用估计在 100 多万美元到近 500 万美元之间。在 SCI 患者护理过程中做出的紧急临床决策（例如在手术和 ICU 管理期间）对于患者的康复至关重要。然而，临床医生缺乏通过数据驱动研究开发的指导。TRACK-SCI 团队特别感兴趣的一个领域是手术过程中的血压管理如何影响患者的康复可能性。
解决方案
TRACK-SCI 团队与 DataRobot 合作应对这一研究挑战，并改进 SCI 患者手术室血压管理指南。TRACK-SCI 团队使用包括受伤性质和位置在内的患者数据，以及手术期间每五分钟记录一次的血压和心率时间序列读数，利用 DataRobot 中的自动特征发现功能发现新特征并建立预测模型来确定患者是否会好转。利用 DataRobot 的预测分析，TRACK-SCI 团队对许多变量对 SCI 患者结果的影响进行了严格检查。一个新颖的见解是，处于高血压状态的时间对患者好转的可能性有显著影响。
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