The invention discloses a
sepsis patient
organ function damage early warning judgment method based on
machine learning, and the method comprises the steps: obtaining the multi-
modal clinical data of a
sepsis patient, including vital sign
time sequence data, inspection data,
treatment intervention data and static patient basic information; after data
standardization processing,
organ function associated features are extracted through a multi-scale
feature fusion strategy, and an organ-level
feature set is constructed; the method comprises the following steps of: obtaining a multi-organ collaborative
early warning model, inputting the multi-organ collaborative
early warning model into a pre-trained multi-organ collaborative
early warning model, respectively constructing an inter-organ compensatory relation map by double branches of the model, quantifying organ
injury risk contribution degree, and outputting a multi-organ functional
injury risk matrix by combining with attention mechanism weighted fusion; and generating an early warning result based on the
risk matrix and the
organ specificity early warning threshold, including the
injury risk level of each organ and the dominant risk key feature identifier,
monitoring data update in real time, and dynamically adjusting the interval to update the early warning result. According to the invention, the limitation of traditional single-
source data and single-organ early warning is broken through, and the early warning accuracy and real-time performance are improved.