将医疗团队协作与患者结局关联以进行预测分析
Associating Healthcare Teamwork with Patient Outcomes for Predictive Analysis
AI总结：
本研究通过电子健康记录系统建模医疗专业人员协作网络，应用机器学习技术检测患者生存预测信号，并识别与改善结局相关的关键网络特征，经临床专家验证其现实应用潜力。
摘要：
癌症治疗结局不仅受临床和人口统计学因素影响，还受医疗团队协作的影响。然而，先前的工作在很大程度上忽视了人类协作在塑造患者生存中的潜在作用。本文提出了一种应用人工智能方法，通过电子健康记录（EHR）系统捕获的医疗专业人员（HCP）协作，揭示其对癌症患者结局的影响。我们将EHR介导的HCP交互建模为网络，并应用机器学习技术检测这些协作中嵌入的患者生存预测信号。我们的模型经过交叉验证以确保泛化能力，并通过识别与改善结局相关的关键网络特征来解释预测。重要的是，临床专家和文献验证了所识别关键协作特征的相关性，增强了其在现实应用中的潜力。这项工作为利用协作的数字痕迹和人工智能评估及改善基于团队的医疗保健提供了实用工作流程。该方法可能可转移到涉及复杂协作的其他领域，并提供可操作的见解以支持医疗保健服务中的数据知情干预。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
