Gui Xiongbin, Lv Hanlin, Wang Xiao, Lv Longting, Xiao Yi, Wang Lei
The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, 530023, China.
Institute of Biointellgence Technology, BGI Research, Wuhan, 430074, China.
BioData Min. 2025 Jun 14;18(1):42. doi: 10.1186/s13040-025-00458-5.
Recruitment for cohorts involving complex liver diseases, such as hepatocellular carcinoma and liver cirrhosis, often requires interpreting semantically complex criteria. Traditional manual screening methods are time-consuming and prone to errors. While AI-powered pre-screening offers potential solutions, challenges remain regarding accuracy, efficiency, and data privacy.
We developed a novel patient pre-screening pipeline that leverages clinical expertise to guide the precise, safe, and efficient application of large language models. The pipeline breaks down complex criteria into a series of composite questions and then employs two strategies to perform semantic question-answering through electronic health records: (1) Pathway A, Anthropomorphized Experts' Chain of Thought strategy; and (2) Pathway B, Preset Stances within an Agent Collaboration strategy, particularly in managing complex clinical reasoning scenarios. The pipeline is evaluated on key metrics including precision, recall, time consumption, and counterfactual inference-at both the question and criterion levels.
Our pipeline achieved a notable balance of high precision (e.g., 0.921, criteria level) and good overall recall (e.g., ~ 0.82, criteria level), alongside high efficiency (0.44s per task). Pathway B excelled in high-precision complex reasoning (while exhibiting a specific recall profile conducive to accuracy), whereas Pathway A was particularly effective for tasks requiring both robust precision and recall (e.g., direct data extraction), often with faster processing times. Both pathways achieved comparable overall precision while offering different strengths in the precision-recall trade-off. The pipeline showed promising precision-focused results in hepatocellular carcinoma (0.878) and cirrhosis trials (0.843).
This data-secure and time-efficient pipeline shows high precision and achieves good recall in hepatopathy trials, providing promising solutions for streamlining clinical trial workflows. Its efficiency, adaptability, and balanced performance profile make it suitable for improving patient recruitment. And its capability to function in resource-constrained environments further enhances its utility in clinical settings.
招募涉及复杂肝脏疾病（如肝细胞癌和肝硬化）的队列研究，通常需要解读语义复杂的标准。传统的人工筛选方法既耗时又容易出错。虽然人工智能驱动的预筛选提供了潜在的解决方案，但在准确性、效率和数据隐私方面仍然存在挑战。
我们开发了一种新颖的患者预筛选流程，该流程利用临床专业知识来指导大语言模型的精确、安全和高效应用。该流程将复杂标准分解为一系列复合问题，然后采用两种策略通过电子健康记录进行语义问答：（1）路径A，拟人化专家的思维链策略；（2）路径B，智能体协作策略中的预设立场，特别是在管理复杂的临床推理场景时。该流程在关键指标上进行评估，包括精度、召回率、时间消耗以及在问题和标准层面的反事实推理。
我们的流程在高精度（例如，标准层面为0.921）和良好的总体召回率（例如，标准层面约为0.82）之间实现了显著平衡，同时具有高效率（每项任务0.44秒）。路径B在高精度复杂推理方面表现出色（同时展现出有利于准确性的特定召回率特征），而路径A对于需要强大精度和召回率的任务（例如直接数据提取）特别有效，处理时间通常更快。两条路径在总体精度上相当，但在精度 - 召回率权衡中具有不同优势。该流程在肝细胞癌（0.878）和肝硬化试验（0.843）中显示出以精度为重点的良好结果。
这种数据安全且高效的流程在肝病试验中显示出高精度并实现了良好的召回率，为简化临床试验工作流程提供了有前景的解决方案。其效率、适应性和平衡的性能特征使其适合改善患者招募。并且它在资源受限环境中运行的能力进一步增强了其在临床环境中的实用性。
