Intelligent identification and early warning method and system for abnormal pattern of venous thrombosis test data

By combining a time-series dependency model and a dynamic early warning index, the problems of time-series changes and multidimensional analysis in venous thrombosis risk assessment are solved, enabling accurate assessment and adaptive optimization of venous thrombosis risk and providing timely intervention suggestions.

CN122245578APending Publication Date: 2026-06-19HANGZHOU XIE TENG MEDICAL TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
HANGZHOU XIE TENG MEDICAL TECH CO LTD
Filing Date
2026-03-06
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Existing methods for assessing the risk of venous thrombosis lack the ability to analyze the temporal relationships between test indicators and to perform multidimensional comprehensive analysis, resulting in insufficient sensitivity and low accuracy. Furthermore, they lack adaptive learning mechanisms, making it difficult to optimize the assessment model.

⚗Method used

A time-series dependency model is used for multi-dimensional correlation analysis to construct a set of feature vectors. A dynamic early warning index is used to reflect the dynamic evolution of indicators. Risks are identified by combining an abnormal pattern knowledge base, and the model is adjusted based on clinical intervention results.

🎯Benefits of technology

It enables accurate assessment of venous thrombosis risk, improves the sensitivity and specificity of early warning, provides targeted intervention suggestions, and the system can adaptively optimize to reduce false alarm rate.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention provides an intelligent identification and early warning method and system for abnormal patterns in venous thrombosis test data, relating to the field of data processing technology. The method includes acquiring venous thrombosis-related test data of a target patient, constructing a feature vector set based on a time-series dependency model, matching it with an abnormal pattern knowledge base to identify risks, calculating a dynamic early warning index, pushing early warning information to a medical decision-making terminal when trigger conditions are met, and adaptively adjusting the model based on clinical intervention results. This invention can improve the accuracy of early identification of venous thrombosis risk, reduce the missed diagnosis rate, and improve the timeliness of clinical intervention.
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