Design of esophageal nursing needs identification model for inpatients based on attention mechanism

In alignment with the scope in Computer Science, which emphasizes intelligent systems and human-centric computing, this study addresses the critical gap in clinically interpretable, physiologically grounded modeling for patient-specific decision support. Conventional computational approaches to esophageal care often rely on static classifiers or linear regressions that lack adaptability, multimodal integration, and physiological interpretability, limiting their deployment in real-time clinical workflows. Moreover, they typically disregard structural constraints intrinsic to the esophagus, resulting in clinically implausible outputs and reduced generalizability across diverse patient populations. An integrated approach combining the Structured Esophageal Dynamics Network (SED-Net) with a Domain-guided Esophageal Adaptation Strategy (DEAS) is developed to tackle prevailing limitations in modeling esophageal dynamics. SED-Net is a modular neural architecture designed to capture the topological and temporal intricacies of esophageal dynamics using graph-based spatial encoding, constrained temporal recurrence, and structure-aware decoding. Complementarily, DEAS injects domain knowledge through physiological constraint regularization, topological flow preservation, hierarchical attention calibration, and patient-specific adaptive feedback. This hybrid system aligns with key of the journal such as biomedical informatics, adaptive systems, and machine learning interpretability. Experimental evaluations demonstrate significant improvements in predictive accuracy, anatomical coherence, and cross-patient generalization, positioning the proposed framework as a promising advancement in computational health informatics for dynamic esophageal assessment and intervention.