Intelligent assessment system and method for F-S-D symptom group of breast cancer chemotherapy patients

By using multidimensional data collection and recursive decision trees to dynamically generate personalized thresholds, and combining this with an LSTM model to correct symptom intensity, the time-consuming and delayed identification problems in assessing FSD symptom clusters in breast cancer chemotherapy patients have been solved, achieving efficient and accurate symptom assessment and intervention.

CN121839145BActive Publication Date: 2026-06-23SHANDONG UNIV
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Patent Information

Application Number
CN202610296955.9
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-12
Publication Date
2026-06-23
Estimated Expiration
2046-03-12

AI Technical Summary

Technical Problem

In existing technologies, the assessment model for FSD symptom clusters in breast cancer chemotherapy patients is time-consuming and lacks specificity. Furthermore, fixed thresholds cannot capture individual differences and symptom fluctuations, leading to delayed identification and missed diagnoses.

Method used

A multidimensional data acquisition module is used to acquire patient symptom data in real time. Personalized thresholds are dynamically generated using the entropy weight method and recursive decision tree. The symptom intensity is corrected by combining an LSTM model, and precise assessment and intervention are carried out through a special assessment scale.

Benefits of technology

It simplifies the assessment process, improves the accuracy of core symptom identification, enables early identification of hidden risks, reduces assessment workload, and improves assessment efficiency and accuracy.

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Abstract

This invention belongs to the field of rehabilitation medicine technology, specifically disclosing an intelligent triage assessment system and method for F-S-D symptom clusters in breast cancer chemotherapy patients. The system includes: a multi-dimensional data acquisition module configured to acquire real-time sensory intensity data of the patient for three dimensions: fatigue, sleep disturbance, and depression, and map them into standardized symptom intensity vectors; a dynamic threshold determination module configured to determine whether to enable a fixed threshold or a dynamic threshold, and calculate a personalized dynamic threshold when enabling a dynamic threshold; a core symptom determination module configured to determine the patient's current core symptoms using a recursive decision tree; and a specialized assessment scale loading module configured to retrieve specialized assessment scales for core symptoms from the cloud for the patient to complete, and generate a structured report with severity gradients based on the completion results. This invention eliminates the need for full-scale quantitative assessment, making the testing process more targeted and reducing the workload of the assessment.
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