用AI分析医生协作网络，预测癌症患者生存率。
Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis
- 将电子病历中的医生互动建模为协作网络，用机器学习挖掘预测信号。
- 跨验证模型显示特定网络特征与患者生存率提升显著相关。
- 结果获临床专家认可，适合用于优化医疗团队协作策略。
癌症治疗效果不仅受临床和人口统计因素影响，也与医疗团队协作密切相关。然而，以往研究大多忽视了人类协作对患者生存的影响。本文提出一种应用型AI方法，通过电子健康记录（EHR）系统捕捉医护人员（HCPs）的协作行为，将其建模为网络，并利用机器学习技术识别嵌入其中的患者生存预测信号。模型经过交叉验证以确保泛化能力，并通过识别与良好预后相关的关键网络特征来解释预测结果。重要的是，这些关键协作特征得到了临床专家和文献的支持，证实其在真实世界中的应用潜力。本研究构建了一套可落地的流程，利用数字协作痕迹与AI评估并改进基于团队的医疗实践，该方法还可推广至其他复杂协作领域，为医疗交付提供数据驱动的干预依据。
原文摘要 · Abstract (English)
Cancer treatment outcomes are influenced not only by clinical and demographic factors but also by the collaboration of healthcare teams. However, prior work has largely overlooked the potential role of human collaboration in shaping patient survival. This paper presents an applied AI approach to uncovering the impact of healthcare professionals' (HCPs) collaboration, captured through electronic health record (EHR) systems, on cancer patient outcomes. We model EHR-mediated HCP interactions as networks and apply machine learning techniques to detect predictive signals of patient survival embedded in these collaborations. Our models are cross validated to ensure generalizability, and we explain the predictions by identifying key network traits associated with improved outcomes. Importantly, clinical experts and literature validate the relevance of the identified crucial collaboration traits, reinforcing their potential for real-world applications. This work contributes to a practical workflow for leveraging digital traces of collaboration and AI to assess and improve team-based healthcare. The approach is potentially transferable to other domains involving complex collaboration and offers actionable insights to support data-informed interventions in healthcare delivery.
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