映射患者轨迹：从患者临床叙述中理解与可视化脓毒症预后路径
Mapping Patient Trajectories: Understanding and Visualizing Sepsis Prognostic Pathways from Patients Clinical Narratives
AI总结：
本文提出一种基于临床叙述和自然语言处理的方法，利用 SHAP 解释不同脓毒症患者亚组，构建并可视化预后路径，揭示疾病进展轨迹与关键影响因素，助力实现个性化医疗。
摘要：
近年来，医疗保健专业人员越来越强调通过探索预后路径来实现个性化和循证的患者护理。为了研究这一点，许多研究人员传统上一直使用来自电子健康记录（EHRs）数据的结构化临床变量。目前，自然语言处理模型在临床研究中受到了广泛关注，这扩大了使用临床叙述的可能性。在本文中，我们提出了一种系统方法，用于开发源自临床记录的脓毒症预后路径，重点关注通过探索与脓毒症相关的合并症而识别出的不同患者亚组，并使用 SHAP 生成这些亚组的解释。这些亚组提取的预后路径为脓毒症严重程度随时间变化的动态轨迹提供了有价值的见解。可视化这些路径阐明了在各种背景下疾病进展的可能性和方向，并揭示了影响脓毒症各阶段之间转变的模式和关键因素或生物标志物，无论是恶化还是改善。这使医疗保健提供者能够为个别患者实施更加个性化和有效的医疗保健策略。
英文摘要：
In recent years, healthcare professionals are increasingly emphasizing on personalized and evidence-based patient care through the exploration of prognostic pathways. To study this, structured clinical variables from Electronic Health Records (EHRs) data have traditionally been employed by many researchers. Presently, Natural Language Processing models have received great attention in clinical research which expanded the possibilities of using clinical narratives. In this paper, we propose a systematic methodology for developing sepsis prognostic pathways derived from clinical notes, focusing on diverse patient subgroups identified by exploring comorbidities associated with sepsis and generating explanations of these subgroups using SHAP. The extracted prognostic pathways of these subgroups provide valuable insights into the dynamic trajectories of sepsis severity over time. Visualizing these pathways sheds light on the likelihood and direction of disease progression across various contexts and reveals patterns and pivotal factors or biomarkers influencing the transition between sepsis stages, whether toward deterioration or improvement. This empowers healthcare providers to implement more personalized and effective healthcare strategies for individual patients.
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。
