Active Learning for Forecasting Severity among Patients with Post Acute Sequelae of SARS-CoV-2
This work addresses the problem of improving patient management and resource allocation for healthcare systems dealing with PASC, but it is incremental as it builds on existing methods with a small dataset.
The study tackled the challenge of accurately identifying progression events like hospitalization and reinfection in patients with Post Acute Sequelae of SARS-CoV-2 (PASC) by introducing a new cohort of 18 patients with text time series features and proposing an Active Attention Network, aiming to enhance clinical risk prediction accuracy and enable progression event identification with fewer annotations.
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.