Information Processing Method for Personalized Treatment Selection
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Solution Overview
Problem
The challenge lies in selecting an appropriate combination of treatments or measures for an individual, especially when the number of options is vast, making it difficult to refer to past cases and tailor solutions effectively.
Innovation Solution
An information processing method that classifies combination information into clusters, generates a first model based on condition information and cluster classification, and a second model using condition information, clusters, and combination information to output relevant combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of selectable treatments is increased to provide more personalized options, then the adaptability of treatment selection is improved, but the device complexity and difficulty of selection increase
Solution Approach 1:
The patent segments the enormous space of treatment combinations by introducing intermediate cluster representations. Instead of directly selecting from all possible treatment combinations, the system divides them into clusters based on treatment characteristics, allowing personalized selection through a hierarchical approach that reduces complexity while maintaining adaptability.
Solution Approach 2:
The patent introduces cluster information as an intermediary between condition information and combination information. The first model maps conditions to clusters, and the second model maps clusters to specific treatment combinations, acting as a mediator that simplifies the selection process while preserving personalized adaptability.
2Adaptability or versatility
If the number of treatment combinations is increased to cover more cases, then the adaptability is improved, but the number of past cases available for reference decreases
Solution Approach 1:
The patent creates cluster representations that serve as simplified copies or abstractions of treatment combination patterns. By learning from past cases and encoding them into cluster prototypes, the system preserves essential information from historical data while being able to generalize to new, unseen treatment combinations, thus maintaining adaptability without losing the benefits of past case knowledge.
3Adaptability or versatility
If the number of treatment combinations is increased to provide more options, then the adaptability is improved, but the ease of operation decreases
Solution Approach 1:
The patent segments the complex selection task into two simpler sub-tasks: first mapping patient conditions to treatment clusters, then mapping clusters to specific treatment combinations. This segmentation reduces the cognitive load and operational difficulty by breaking down the enormous combination space into manageable steps, improving ease of operation while maintaining the availability of numerous personalized options.
Solution Approach 2:
The cluster representation serves as an intermediary that simplifies the selection process. Instead of directly navigating through all possible treatment combinations, the system uses clusters as intermediate steps that are easier to work with, reducing the operational complexity while preserving access to a wide range of personalized treatment options.
Data Source
AI summary
An information processing apparatus of the present invention includes: a clustering unit that classifies combination information representing a combination of a plurality of types of measures performed on a target person for each time, as any one of a plurality of clusters set in advance; a first model generating unit that generates a first model based on condition information representing a condition of the target person and the cluster as which the combination of the plurality of types of measures performed on the target person is classified for each time, the first model outputting the cluster for the condition information; and a second model generating unit that generates a second model based on the condition information, the cluster, and the combination information for each time, the second model outputting the combination information for information based on the condition information and the cluster.