Learning Model Generation Apparatus for Personalized Treatment Plans
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating treatment plans, such as in the medical field, rely heavily on human expertise and are burdensome, with quality influenced by the doctor's experience, and lack the ability to create personalized plans efficiently, especially considering individual patient characteristics and gene-related differences.
Innovation Solution
A learning model generation apparatus and method that dynamically generates action plans by moving samples with high output errors from a target sample group to a source sample group, generating weak learners, and creating a learning model based on these learners to optimize action predictions for each individual, using observation data that includes states and actions over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a doctor manually analyzes patient information and creates a treatment plan according to treatment guidelines, then the treatment plan can be tailored to the patient, but the preparation burden is large and quality is influenced by the doctor's experience
Solution Approach 1:
The system enables automatic generation of treatment plans through a learning model that processes patient data and generates personalized action plans without requiring manual analysis by doctors. The learning model autonomously performs the task of creating tailored treatment plans based on observed patterns in patient data, thereby reducing the preparation burden while maintaining quality consistency.
Solution Approach 2:
The manual mechanical process of doctor analysis and plan creation is replaced by an automated learning model system. The learning model uses machine learning algorithms to process patient information and generate treatment plans automatically, substituting the mechanical manual work with an automated intelligent system that operates consistently without human experience limitations.
2Productivity
If a learning model is generated using only target sample group data, then the model can be trained efficiently, but samples with high output errors cannot be utilized to improve the model
Solution Approach 1:
The system implements feedback by moving samples with high output errors from the target sample group to the source sample group. This feedback mechanism allows the system to learn from its mistakes by using misclassified samples to improve future model iterations, thereby enhancing model accuracy while maintaining efficient training processes.
Solution Approach 2:
The system dynamically adjusts the composition of sample groups by transferring samples based on their performance characteristics. Samples are dynamically reclassified from target to source groups based on output error thresholds, allowing the training process to adapt and evolve continuously, improving model accuracy over time without sacrificing generation efficiency.
Data Source
AI summary
A machine learning model generation apparatus includes: a movement unit that performs movement processing of moving a sample, having an output error of a (t+1)-th order machine learning model with respect to observation data at time t+1 being larger than a predetermined amount, from the target sample group to a source sample group; and a generation unit that generates a plurality of weak learners by using at least observation data of a sample included in the target sample group after the movement processing and a sample included in the source sample group after the movement processing, and generates a t-th order machine learning model, based on at least each of the plurality of weak learners, and a classification error being evaluated, for each of the plurality of weak learners, by using observation data at time t of the sample included in the target sample group after the movement processing.