Patient Subtype Classification via Quantitative Sub-cohort Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current definitions of diseases and health conditions are often broad or imprecise, leading to varying patient responses to treatments and outcomes, as well as potential side effects and suboptimal results due to inadequate accounting for comorbidities and parallel treatments.
Innovation Solution
A classification system that uses quantitative definitions of patient subtypes, derived by grouping patients into sub-cohorts based on similar medical fact patterns, to provide more precise and less broad definitions of medical conditions, thereby improving treatment efficacy and outcome prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If current broad definitions of diseases and health conditions are used, then the classification system is simple and easy to operate, but the measurement precision and manufacturing precision of patient classification deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the broad disease category into multiple distinct subtypes based on quantitative patient characteristics. Instead of using a single broad definition, the system segments patients into sub-cohorts with specific quantitative criteria (e.g., HbA1c levels, weight loss rates, treatment responses), thereby improving measurement precision while maintaining operational simplicity through automated classification algorithms
2Device complexity
If current broad definitions of diseases and health conditions are used, then the device complexity is low, but the manufacturing precision of patient classification deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming qualitative disease definitions into quantitative parameters. The system uses measurable parameters such as HbA1c levels, weight loss rates, and treatment response metrics to precisely classify patients into subtypes. This parameter-based approach enables high manufacturing precision in patient classification while keeping device complexity manageable through standardized measurement protocols
3Ease of operation
If imprecise definitions of diseases and health conditions are used, then the ease of operation is maintained, but the reliability of treatment outcomes deteriorates
Solution Approach 1:
The patent applies feedback by incorporating treatment outcome data into the classification system. The system continuously monitors patient responses to treatments and uses this feedback to refine and update subtype definitions. This feedback mechanism improves the reliability of treatment outcomes by allowing the classification system to adapt to real-world treatment effectiveness while maintaining ease of operation through automated iterative refinement
4Quantity of substance
If broad definitions of diseases and health conditions are used, then the quantity of patients in each category is large, but the homogeneity of patient characteristics within categories deteriorates
Solution Approach 1:
The patent applies segmentation by dividing large patient populations into smaller, more homogeneous sub-cohorts based on quantitative characteristics. Each subtype represents a segment with more uniform patient characteristics (e.g., similar HbA1c ranges, weight loss rates, or treatment responses), thereby improving homogeneity while maintaining adequate sample sizes through hierarchical classification structures
Data Source
AI summary
A classification method and system for medical conditions based on the concept of subtypes, which are classes of patients whose medical fact patterns as analyzed in an N-dimensional space places them closer to other patients belonging to the same subtype than to patients who belong to different subtypes and, who share similar likelihood of certain specified outcomes. A computer system processes patient data for a plurality of patients from a set of patients called a cohort. The computer system processes the patient data for the cohort to group patients into sub-cohorts of similar patients, i.e., each sub-cohort includes patients who have similar medical fact patterns in their patient data. Patients in different sub-cohorts generally, but not necessarily, have significant differences in their patient data. The computer system generates quantitative definitions, describing the patients in the sub-cohorts.