Wang Jing, Sra Amar, Weiss Jeremy C
National Library of Medicine, Bethesda, MD.
The George Washington University, Washington, DC.
ArXiv. 2025 Jun 11:arXiv:2506.22444v1.
The long-term effects of Postacute Sequelae of SARS-CoV-2, known as PASC, pose a significant challenge to healthcare systems worldwide. Accurate identification of progression events-such as hospitalization and reinfection-is essential for effective patient management and resource allocation. However, traditional models trained on structured data struggle to capture the nuanced progression of PASC. In this study, we introduce the first publicly available cohort of 18 PASC patients, with text time series features based on Large Language Model Llama-3.1-70B-Instruct and clinical risk annotated by clinical expert. We propose an Active Attention Network to predict the clinical risk and identify progression events related to the risk. By integrating human expertise with active learning, we aim to enhance clinical risk prediction accuracy and enable progression events identification with fewer number of annotation. The ultimate goal is to improves patient care and decision-making for SARS-CoV-2 patient.
新冠病毒急性后遗症（PASC）的长期影响给全球医疗系统带来了重大挑战。准确识别病情进展事件，如住院和再次感染，对于有效的患者管理和资源分配至关重要。然而，基于结构化数据训练的传统模型难以捕捉PASC细微的病情进展。在本研究中，我们引入了首个公开可用的由18名PASC患者组成的队列，其具有基于大语言模型Llama-3.1-70B-Instruct的文本时间序列特征，并由临床专家标注了临床风险。我们提出了一种主动注意力网络来预测临床风险并识别与风险相关的病情进展事件。通过将人类专业知识与主动学习相结合，我们旨在提高临床风险预测的准确性，并以更少的标注数量实现病情进展事件的识别。最终目标是改善对新冠病毒患者的护理和决策。
