Xinsong Du, John Novoa-Laurentiev, Joseph M Plasek, Ya-Wen Chuang, Liqin Wang, Frank Chang, Surabhi Datta, Hunki Paek, Bin Lin, Qiang Wei, Xiaoyan Wang, Jingqi Wang, Hao Ding, Frank J Manion, Jingcheng Du, Li Zhou
SUMMARY: We found LLM, traditional machine learning, and deep learning had diverse error profiles on cognitive decline identification from clinical notes, and the ensemble of LLM, machine learning, and deep learning achieved state of the art performance. BACKGROUND: Early detection of cognitive decline in elderly individuals facilitates clinical trial enrollment and timely medical interventions. This study aims to apply, evaluate, and compare advanced natural language processing techniques for identifying signs of cognitive decline in clinical notes...
April 5, 2024: medRxiv