A Radiotherapy Nutritional Risk Assessment System and Method Based on Multimodal Data
By constructing a radiotherapy nutrition risk assessment system based on multimodal data, integrating multimodal data and introducing an attention mechanism, the system addresses the issues of lag and one-sidedness in existing nutritional risk assessment technologies. This enables accurate prediction of nutritional risks for radiotherapy patients and personalized intervention recommendations, thereby improving the timeliness and precision of nutritional support.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NANTONG TUMOR HOSPITAL
- Filing Date
- 2026-03-17
- Publication Date
- 2026-05-26
AI Technical Summary
In existing technologies, nutritional risk assessment for radiotherapy patients mainly relies on single or static clinical indicators, lacking integrated analysis of multimodal time-series data. This results in delayed and one-sided risk assessment, making it impossible to accurately predict the evolution trajectory of patients' nutritional risks, and intervention recommendations lack individualization and dynamic adjustment capabilities.
The radiotherapy nutrition risk assessment system based on multimodal data integrates structured clinical data, unstructured text records, and time-series radiomics data of radiotherapy patients to construct a multimodal high-dimensional feature tensor time-series sequence. It introduces an attention mechanism to fusion network adaptively learn the dynamic weights of each modality feature, generates a weighted fusion multimodal feature vector, establishes a radiotherapy nutrition risk time-series prediction model, and generates individualized nutrition intervention suggestions by associating with the hospital's nutrition intervention knowledge base.
It enables a detailed portrayal of the patient's condition throughout the entire life cycle, improves the accuracy of nutritional risk level prediction and the identification of risk evolution trajectory, generates highly individualized and dynamically adjustable nutritional intervention recommendations, and realizes a shift in decision-making mode from passive response to proactive early warning.
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