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image of Clinical Diagnosis and Treatment System for Neurological Psychological Gastrointestinal Diseases Based on Multimodal Artificial Intelligence and Immunology

Abstract

Objective

To predict and assist in the treatment of colorectal cancer.

Background

Precision medicine systems can provide strategy optimization for the diagnosis and treatment of colorectal cancer to meet the needs of clinical pricing institutions.

Aim

To design an artificial intelligence multimodal gastrointestinal disease clinical information system based on neuroimmune gene regulation.

Methods

The system includes the use of cell gene expression levels to predict the 5-year survival rate of cancer patients, and the development of disease incidence rate prediction models based on immune cell status and living habits in somatic cell testing. The biological mechanism of feature selection in survival prediction systems was elucidated using single-cell sequencing technology, and this mechanism was analyzed in depth using molecular simulation techniques. Based on NCAM1 and CADM1 genes, biological activation pathway analysis was conducted to explore the biological mechanism of their synergistic immune genome regulation of gastrointestinal tumor proliferation.

Results

The accuracy of each model is higher than 0.70. The experimental credibility is high.

Conclusion

The research team conducted a detailed analysis of the biological characteristics of AI algorithms and reached a consensus with clinical experts. The ethical approval number of Chifeng Cancer Hospital is 202401, which has been reported by the World Health Organization.

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2025-04-29
2025-09-11

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Clinical Diagnosis and Treatment System for Neurological Psychological Gastrointestinal Diseases Based on Multimodal Artificial Intelligence and Immunology
Bentham Science Publishers ; https://doi.org/10.2174/0113892010359130250413024112
/content/journals/cpb/10.2174/0113892010359130250413024112
/content/journals/cpb/10.2174/0113892010359130250413024112
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  • Article Type:     Research Article
Keywords: gastrointestinal oncology ; tumor surgery ; clinical diagnosis ; early cancer dissection ; cancer prognosis ; Computational biology

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