用主动学习提升新冠后遗症患者风险预测精度，减少标注需求。
Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
- 结合专家标注与主动学习，构建注意力网络预测病情进展风险。
- 基于18名患者文本时序数据，实现对住院、再感染等事件的精准识别。
- 适合医疗AI研究者及临床决策支持系统开发者参考。
新冠后遗症（PASC）长期影响全球医疗系统。准确识别住院、再感染等进展事件对患者管理与资源分配至关重要。传统基于结构化数据的模型难以捕捉PASC的复杂演变过程。本研究首次公开一个包含18名患者的PASC队列，利用大语言模型Llama-3.1-70B-Instruct生成文本时序特征，并由临床专家标注临床风险。提出主动注意力网络（Active Attention Network），融合人类专家知识与主动学习机制，以更少标注量提升临床风险预测准确性，实现进展事件的有效识别。目标是改善新冠患者诊疗与决策支持。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。