A clinical parameter-based liver and gall postoperative infection risk grading early warning method

By employing a multidimensional distribution mapping algorithm and a dynamic weighting mechanism, the shortcomings of traditional methods for assessing postoperative infection risk in hepatobiliary surgery, such as insufficient nonlinear correlation capture and parameter adjustment, have been addressed. This approach enables highly accurate and adaptive risk assessment, supporting real-time clinical monitoring and intervention.

CN121983322BActive Publication Date: 2026-07-10FOURTH MILITARY MEDICAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
FOURTH MILITARY MEDICAL UNIVERSITY
Filing Date
2026-04-08
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Traditional methods for assessing the risk of postoperative infection in hepatobiliary surgery cannot capture the complex high-order interactions and nonlinear relationships between clinical parameters. The parameter weights are fixed and cannot be adjusted in real time. They also lack clear risk grading rules, resulting in insufficient prediction accuracy and limited value for engineering applications.

Method used

A multidimensional distribution mapping algorithm is used to quantify the abnormal deviation of clinical parameters. Combined with a dynamic weighting mechanism and a time correction term, a score scale is generated. The weights are adjusted by a hyperbolic tangent function to realize risk level classification and real-time intervention strategies.

Benefits of technology

It improves the accuracy and adaptability of assessments, can adjust parameter weights in real time, provides clear risk grading rules, reduces the misjudgment rate, and supports real-time clinical monitoring and intervention.

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Abstract

The present application relates to the field of postoperative infection risk grading and early warning of hepatobiliary surgery, and in particular to a postoperative infection risk grading and early warning method based on clinical parameters. The content includes: collecting original clinical parameters and preprocessing to obtain preprocessed standardized clinical parameters; introducing a clinical basic weight, based on the preprocessed standardized clinical parameters, applying a multidimensional distribution mapping algorithm to calculate an infection risk score; dividing the risk level based on the infection risk score, and introducing a dynamic weighting mechanism with emphasis on effective updating to dynamically update the clinical basic weight. The method solves the problems of traditional postoperative infection risk assessment methods for hepatobiliary surgery, such as ignoring the complex high-order interaction and nonlinear correlation between body temperature, white blood cell count, procalcitonin, C-reactive protein and other indicators, being unable to adjust the parameter weight in real time according to the dynamic changes of the patient's postoperative condition, and lacking clear risk grading rules and limited engineering application value.
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