Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial obstacles to effective visual analytics and decision-making. Through a year-long collaboration with experienced neurologists, we developed PhenoFlow, a visual analytics system that leverages the collaboration between human and Large Language Models (LLMs) to analyze the extensive and complex data of acute ischemic stroke patients. PhenoFlow pioneers an innovative workflow, where the LLM serves as a data wrangler while neurologists explore and supervise the output using visualizations and natural language interactions. This approach enables neurologists to focus more on decision-making with reduced cognitive load. To protect sensitive patient information, PhenoFlow only utilizes metadata to make inferences and synthesize executable codes, without accessing raw patient data. This ensures that the results are both reproducible and interpretable while maintaining patient privacy. The system incorporates a slice-and-wrap design that employs temporal folding to create an overlaid circular visualization. Combined with a linear bar graph, this design aids in exploring meaningful patterns within irregularly measured BP data. Through case studies, PhenoFlow has demonstrated its capability to support iterative analysis of extensive clinical datasets, reducing cognitive load and enabling neurologists to make well-informed decisions. Grounded in long-term collaboration with domain experts, our research demonstrates the potential of utilizing LLMs to tackle current challenges in data-driven clinical decision-making for acute ischemic stroke patients.

翻译：急性卒中需要及时诊断与治疗以实现最佳患者预后。然而，与急性卒中相关的临床数据（尤其是血压测量数据）具有复杂且不规则的特点，这为有效的可视化分析与临床决策带来了显著挑战。通过与经验丰富的神经科医生开展为期一年的合作，我们开发了PhenoFlow——一个利用人类与大型语言模型（LLM）协同机制来分析急性缺血性卒中患者海量复杂数据的可视化分析系统。PhenoFlow开创了一种创新工作流程：LLM充当数据整理器，神经科医生则通过可视化界面和自然语言交互来探索和监督输出结果。该方法使神经科医生能够以更低的认知负荷更专注于临床决策。为保护敏感患者信息，PhenoFlow仅利用元数据进行推理和生成可执行代码，而不访问原始患者数据。这既确保了结果的可复现性与可解释性，又严格维护了患者隐私。系统采用切片-包裹式设计，通过时间折叠技术生成叠加式环形可视化视图。结合线性条形图，该设计有助于在不规则测量的血压数据中探索有意义的变化模式。案例研究表明，PhenoFlow能够支持对大规模临床数据集的迭代分析，在降低认知负荷的同时帮助神经科医生做出基于充分证据的决策。基于与领域专家的长期合作，本研究证明了利用LLM应对当前急性缺血性卒中患者数据驱动临床决策挑战的巨大潜力。