ML Patient Data Segmentation for Storage Efficiency
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Solution Overview
Problem
Current data processing systems face challenges in efficiently classifying and segmenting patient data for personalized medical treatment approaches, as they often require significant storage and computational resources when dealing with numerous dimensions, leading to difficulties in generating accurate outputs without excessive resource consumption.
Innovation Solution
Implementing a machine learning-based system that classifies patients into segments based on attitudes and abilities, using a multi-class model to predict segment affiliation, thereby organizing patient data and reducing resource requirements by segmenting the data, allowing for personalized messaging and service delivery tailored to specific patient needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If patient data is processed using traditional data processing systems, then comprehensive patient data can be stored and accessed, but significant storage and computational resources are consumed
Solution Approach 1:
The patent segments patient data into distinct groups based on shared characteristics, attitudes, and abilities. By dividing the homogeneous patient population into heterogeneous segments, the system reduces the computational complexity of processing individual patient records while maintaining comprehensive data availability. Each segment can be processed independently, significantly reducing storage and computational resource requirements.
Solution Approach 2:
The patent transforms patient data from individual-level detailed records to segment-level aggregated characteristics. By changing the parameter representation from individual patient attributes to segment-level summaries (such as segment IDs, aggregate statistics, and common characteristics), the system achieves efficient processing with reduced resource consumption while preserving the ability to access comprehensive patient information when needed.
2Adaptability or versatility
If patient data is segmented into multiple groups, then personalized treatment approaches can be developed, but the complexity of data classification increases
Solution Approach 1:
The patent applies segmentation by dividing the patient population into distinct groups based on shared characteristics such as attitudes toward medical care, abilities to receive care, health engagement levels, and access to resources. This segmentation enables personalized treatment approaches for each segment while using clear, well-defined classification criteria that manage system complexity through structured grouping rather than individualized analysis.
Solution Approach 2:
The patent creates a universal classification framework that can be applied across diverse patient populations and treatment contexts. The segment-based approach provides a multi-functional system that can accommodate different treatment types, patient conditions, and care settings while maintaining a consistent classification structure, thereby reducing overall system complexity through standardized procedures.
3Loss of information
If traditional data processing methods are used, then all patient data can be analyzed, but resource consumption becomes excessive
Solution Approach 1:
The patent changes the parameter representation from individual patient-level detailed data to segment-level aggregated data. By transforming the data parameters to represent groups rather than individuals, the system maintains analytical completeness for population-level insights while dramatically reducing computational energy consumption. The segment characteristics preserve essential information patterns without requiring processing of every individual record.
Solution Approach 2:
The patent extracts essential characteristics and patterns from individual patient data to create segment-level representations. By taking out only the critical information needed for analysis (segment assignments, aggregate statistics, common characteristics) while leaving detailed individual records aggregated, the system achieves complete data analysis capability with minimal energy consumption, extracting only the necessary information for treatment decision-making.
Data Source
AI summary
A system includes one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive data describing a plurality of patients from one or more data sources. The instructions cause the one or more processors to classify the plurality of patients into a plurality of segments describing attitudes and abilities of patients based on execution of a model trained based on a machine learning process and based on the data. The instructions cause the one or more processors to construct a database. The instructions cause the one or more processors to update the database to store classifications of the plurality of patients into the plurality of segments. The instructions cause the one or more processors to construct output data based on the classifications of the plurality of patients into the plurality of segments stored in the database.