Tumor nursing data processing system based on cloud computing

Through the cloud-based tumor nursing data processing system, multi-scale feature analysis and dynamic feature alignment are used to solve the problems of difficult to capture the characteristic changes of nursing data in the existing technology and insufficient consideration of the correlation of data integration, and the accurate analysis and efficient storage of nursing data are achieved, and the timeliness and accuracy of nursing decisions are improved.

CN119649971AActive Publication Date: 2025-03-18NANTONG UNIV +1
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Patent Information

Application Number
CN202411760982.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-03
Publication Date
2025-03-18
Estimated Expiration
2044-12-03

AI Technical Summary

Technical Problem

现有技术难以全面捕捉不同时间尺度内的护理数据特征，忽视了短期与长期特征的变化规律，导致护理效果的精确评估受限，且跨患者数据整合未能充分考虑特征间的关联性与重要性排序，导致特征融合结果难以支持复杂护理数据的精准分析。

Method used

The tumor care data processing system based on cloud computing is adopted, including multi-scale feature analysis module, dynamic feature alignment adjustment module, non-uniform grid storage module and adaptive data partitioning and optimization module. Through multi-scale feature extraction and importance sorting, the feature matching relationship is dynamically updated, the data storage hierarchy is optimized, and data changes are monitored in real time to identify patient response patterns.

Benefits of technology

Effectively capture feature changes in different time dimensions, improve the comprehensiveness and accuracy of feature recognition, improve the comparability and consistency of cross-patient data, significantly improve the accuracy and adaptability of data integration, optimize storage efficiency and resource utilization, and ensure the timeliness and accuracy of data processing and updates.

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Abstract

The invention relates to the technical field of medical data processing, in particular to a tumor nursing data processing system based on cloud computing, and the system comprises a multi-scale feature analysis module which obtains tumor nursing data, splits the tumor nursing data into a plurality of time dimensions, extracts multi-scale features of the tumor nursing data, and sends the multi-scale features to a cloud computing module; and carrying out importance sorting on the multi-scale features of the tumor nursing data to obtain a preliminary tumor nursing data feature alignment result. According to the method, feature decomposition and analysis of multiple time scales are carried out on the tumor nursing data, feature changes in different time dimensions are effectively captured, and the comprehensiveness of feature recognition is enhanced. According to the method, important features are sorted, the matching relation is dynamically adjusted, feature combinations with high relevance are subjected to weighted fusion, meanwhile, the large-scale data processing efficiency is improved through distributed optimization, and the accuracy and adaptability of data integration are remarkably improved.
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