Pediatric nursing quality evaluation method based on multi-modal data interaction

Through multi-modal data interaction method, multi-source nursing data is processed, the problem of subjectivity of traditional nursing quality assessment and difficulty in a unified analysis of data is solved, the accuracy and intelligent monitoring of nursing quality are achieved, and the scientific feedback ability of the nursing process is improved.

CN120413044AActive Publication Date: 2025-08-01晋江市医院(上海市第六人民医院福建医院)
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Patent Information

Application Number
CN202510885149.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-30
Publication Date
2025-08-01
Estimated Expiration
2045-06-30

AI Technical Summary

Technical Problem

Traditional nursing quality assessment relies on manual observation, which is highly subjective and inefficient. Multi-source heterogeneous data is difficult to analyze and utilize. Nursing personnel are not standardized and lack monitoring, which affects nursing quality and infant safety.

Method used

Multi-modal data interaction method is adopted to process multi-source data through ResNet50 model, Glove model and Mel frequency cepspectral coefficients, multi-modal feature vectors are constructed, and nursing behavior and infant state features are extracted and fused using multi-grained feature interaction fusion method to establish a nursing quality assessment model.

Benefits of technology

Time synchronization and structured representation of multi-source data are realized, the accuracy and intelligence of nursing quality assessment are improved, and real-time monitoring and scientific feedback support are realized.

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Abstract

The invention relates to the technical field of medical care, in particular to a pediatric care quality evaluation method based on multi-modal data interaction. The method comprises the steps of multi-modal data acquisition, data preprocessing, nursing behavior feature analysis, infant state evaluation, and nursing quality scoring and feedback. According to the scheme, the multi-source heterogeneous data in the nursing process is collected, the ResNet50 model, the Glove model and the Mel-frequency cepstral coefficient are used for preprocessing and feature extraction of the multi-modal data of the image, the audio and the text, and the integrity and the analysis efficiency of feature fusion are improved; according to the scheme, a multi-granularity feature interactive fusion method is adopted, key features in the nursing process are extracted and fused from multiple dimensions, and the nursing behavior features and the infant state features are comprehensively obtained, so that the accuracy and the intelligent level of nursing quality evaluation are remarkably improved, and real-time monitoring and scientific feedback support of the nursing process are achieved.
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