Intestinal flora transplantation donor-receptor matching method, equipment, medium and product

By constructing a donor-receptor matching model for intestinal microbiota transplantation based on deep capsule network and Bayesian algorithm, the inaccurate problem of donor screening and efficacy prediction in the prior art is solved, and more efficient intestinal microbiota transplantation matching and efficacy prediction are achieved.

CN119763805APending Publication Date: 2025-04-04SHANGHAI TENTH PEOPLES HOSPITAL
0 Cites 4 Cited by

Patent Information

Application Number
CN202411813571.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-11
Publication Date
2025-04-04

AI Technical Summary

Technical Problem

The existing intestinal microbiota transplantation technology lacks systematic standards and scientific basis in donor screening, efficacy prediction and donor-receptor matching, resulting in inconsistent treatment effects and even adverse reactions.

Method used

By obtaining intestinal microbiota data, clinical characteristics and immune inflammation indicators of target receptors and donors, a deep capsule network and Bayesian feature selection algorithm is used to construct a therapeutic effect prediction model and donor-receptor matching model to achieve accurate donor screening and matching.

Benefits of technology

It improves the matching accuracy of intestinal microbial transplantation, enhances the accuracy of efficacy prediction, reduces the side effects of transplant mismatch, and improves the cure rate and application efficiency.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses an intestinal flora transplantation donor-receptor matching method, equipment, a medium and a product, and relates to the technical field of intestinal flora transplantation. The method comprises the following steps: calculating flora characteristics, clinical characteristics and immune inflammation indexes of a target receptor according to the flora characteristics, clinical characteristics and immune inflammation indexes of the target receptor; inputting the to-be-determined donors as well as the flora characteristics, the demographic characteristics and the immune inflammation indexes of the current donor into a curative effect prediction model, and updating the current donor until all to-be-determined donors are traversed; determining the undetermined donors with effective curative effect prediction results as primary selection donors; respectively inputting the flora characteristics, the clinical characteristics and the immune inflammation indexes of the target receptor and the flora characteristics, the demographic characteristics and the immune inflammation indexes of each primarily selected donor into a donor-receptor matching model by taking the flora characteristics, the clinical characteristics and the immune inflammation indexes of the target receptor and the flora characteristics, the demographic characteristics and the immune inflammation indexes of each primarily selected donor as input quantities to obtain a matching score of each primarily selected donor and the target receptor; determining the primary donor with the highest matching score as a target donor; by constructing the curative effect prediction model and the donor-receptor matching model, the matching precision of intestinal flora transplantation is improved.
Need to check novelty before this filing date? Find Prior Art