Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients

Transformer representation learning is necessary for dynamic multi-modal physiological data on small-cohort patients Bingxu Wang, Min Ge, Kunzhi Cai, Yuqi Zhang, Zeyi Zhou, Wenjiao Li, Yachong Guo,, Wei Wang,, and Qing Zhou, Department of Thoracic and Cardiovascular Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Kuang Yaming Honors School, Nanjing University, Nanjing 210023, China National Laboratory of Solid State Microstructure, Department of Physics, Nanjing University, Nanjing 210093, China E-mail: yguo@nju.edu.cn; Abstract Postoperative delirium (POD), a severe neuropsychiatric complication affecting nearly 50% of high-risk surgical patients, is defined as an acute disorder of attention and cognition, It remains significantly underdiagnosed in the intensive care units (ICUs) due to subjective monitoring methods. Early and accurate diagnosis of POD is critical and achievable. Here, we propose a POD prediction framework comprising a Transformer representation model followed by traditional machine learning algorithms. We curated the first multi-modal POD dataset encompass-1 ing two patient types and evaluated the various Transformer architectures for representation learning. Empirical results indicate a consistent improvements of sensitivity and Youden index in patient TYPE I using Transformer representations, particularly our fusion adaptation of Pathformer. By enabling effective delirium diagnosis from postoperative day 1 to 3, our extensive experimental findings emphasize the potential of multi-modal physiological data and highlight the necessity of representation learning via multi-modal Transformer architecture in clinical diagnosis. Introduction Postoperative delirium(POD), a prevalent acute neuropsychiatric syndrome 1,2, affects more than 50% of surgical patients and significantly elevates morbidity and mortality risks 3 . Early identification is crucial yet challenging 4, primarily due to subjective assessment criteria and incomplete understanding of underlying pathophysiological mechanisms 5 .