This application provides a multi-agent, multimodal collaborative method, device, equipment, and storage medium for diagnosing the condition of patients in the
intensive care unit (ICU), belonging to the field of medical
artificial intelligence and intelligent analysis technology for
intensive care. The method includes: receiving multimodal clinical
monitoring data in an ICU setting, including bedside medical images, continuous
life time series, and critical care clinical text; identifying and adaptively routing the input data through a modality detection agent, distributing it to corresponding domain expert agents for
pathological and physiological
feature extraction; constructing a patient-centric graph structure representing the dynamic clinical condition of
ICU patients through a
knowledge graph agent based on the features output by each domain expert agent, this graph structure integrating structured
medical knowledge of entity extraction and
relational reasoning; further, inputting the constructed graph structure and original multimodal features into a collaborative agent, generating the final ICU patient condition diagnosis result (such as mortality prediction, ICU long-stay prediction, etc.) and a traceable reasoning path through
graph traversal and collaborative reasoning. This application effectively breaks down data silos between different monitoring devices in the
intensive care unit environment, dynamically captures cross-
modal associations specific to
critically ill patients, and significantly improves the accuracy of critical care
clinical diagnosis and medical trust.