Immune Data Treatment Matching Using Dedicated Learned Models
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Solution Overview
Problem
Existing technologies fail to effectively associate and learn immune data with treatment guidelines, preventing the extraction and provision of suitable treatments for patients.
Innovation Solution
A system that acquires immune data and treatment guidelines, creates a learning dataset, learns the dataset, generates a model, determines treatment guidelines based on new immune data, and provides the appropriate treatment to healthcare professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing technologies are used for medical support, then general diagnostic and treatment proposals can be provided, but immune data cannot be effectively associated and learned with treatment guidelines
Solution Approach 1:
The system performs preliminary actions by creating a dedicated learning dataset that associates immune data with treatment guidelines before actual treatment recommendation. The learning model is trained in advance on this curated dataset, enabling the system to accurately match new immune data with appropriate treatments when needed.
Solution Approach 2:
The patent introduces an intermediary learning model that mediates between immune data and treatment guidelines. This model acts as a bridge, learning the complex relationships between immune status and appropriate treatments, thereby enabling accurate treatment recommendations without directly linking raw immune data to treatment protocols.
2Adaptability or versatility
If a comprehensive system is built to associate immune data with treatment guidelines, then suitable treatments can be extracted and provided, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: a data acquisition module that collects immune data and treatment guidelines, a learning model training module that creates the association, and a treatment recommendation module that applies the learned model. This segmentation allows each component to be developed and maintained independently, reducing overall system complexity.
Solution Approach 2:
The patent uses copying by creating a learned model that replicates the complex relationships between immune data and treatment guidelines. Once the model is trained, it can be copied and deployed to make treatment recommendations without requiring the original comprehensive dataset or complex processing logic, simplifying the operational system.
Data Source
AI summary
The system for providing immune data treatment acquires immune data and treatment guideline data and then creates a learning dataset associated with the acquired immune data and treatment guideline data. The system learns the created learning data set and generates a learned model based on the learning result. New immune data of a new patient is acquired and the system determines a treatment guideline data part corresponding to the acquired new immune date by using the generated learned model. A treatment is extracted and included in the determined treatment guideline data part and is provided to a doctor or medical institution.