Abstract
BACKGROUND: Acute pancreatitis (AP) morbidity has been increasing in recent years. Patients with AP exhibit highly variable symptom patterns over time, posting challenges to traditional analytical methods. Dynamic Time Warping (DTW) effectively aligns temporal sequences of different rhythms, offering a novel approach to model these complex dynamics. OBJECTIVE: This study employs DTW technology to systematically analyze the individualized developmental trajectories of symptom clusters in patients with AP, delving into the heterogeneous characteristics during the process of time series changes. METHODS: In a longitudinal study of 155 patients with AP, 32 symptoms were assessed using the Memorial Symptom Assessment Scale at hospitalization and 1, 3, 6, 9, and 12 months post-discharge. DTW was used to analyze temporal dynamics, generating individual symptom distance matrices. At the group level, these matrices are integrated using Distatis analysis, followed by hierarchical clustering to identify symptom clusters and network analysis to determine central symptoms. RESULTS: Each patient had unique symptom manifestations and dynamic change patterns. Six major symptom clusters were identified: emotional disorder cluster, appetite disorder cluster, multi-system physical discomfort cluster, localized physiological perception abnormality cluster, functional decline cluster, and abdominal discomfort cluster. Centrality analysis revealed that the appetite domain exhibited high centrality, suggesting that its variation may influence multiple aspects of patient experience. CONCLUSION: Dynamic Time Warping provides a novel and effective approach for analyzing the temporal trajectories of symptoms both within and across individuals. The research results provide methodological support and empirical evidence for individualized symptom management, early intervention, and predictive model construction of AP progression.