Phenotype Vector Manipulation for Medical Data Analysis
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Solution Overview
Problem
Existing methods for calculating inferred selection criteria and constructing analytical variables from observed populations are inefficient and lack scalability, particularly when dealing with a large number of attributes, leading to costly and labor-intensive manual efforts.
Innovation Solution
The system defines phenotypes to create a fundamental atomic building block for data subset creation and vector creation, using phenotype vectors as the primary raw material for EMR-based data science, enabling a systematic process to determine significant factors for approximating a patient population group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to calculate inferred selection criteria and construct analytical variables, then flexibility and customization are maintained, but productivity is low and labor costs are high
Solution Approach 1:
The system performs automatic data manipulation and vector production without requiring manual expert intervention. The phenotype vector generation process self-services by automatically calculating inferred selection criteria, constructing analytical variables, and producing phenotype vectors from source data, thereby eliminating the need for costly expert labor while maintaining high productivity
Solution Approach 2:
The patent replaces manual mechanical data processing with an automated computational system. Instead of experts manually calculating selection criteria and constructing variables, the system uses algorithmic processes to automatically generate phenotype vectors, substituting human expertise with a reproducible mechanical system
2Productivity
If automated phenotype vector generation is implemented, then productivity and scalability are improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex data processing task into distinct components: source data retrieval, phenotype definition application, analytical variable construction, and phenotype vector generation. This segmentation allows each component to be independently managed and optimized, improving overall system productivity while making the complexity more tractable
Solution Approach 2:
The phenotype vector generation system is designed to be universal and applicable to multiple different data sets and study types. The same core system can generate phenotype vectors for various phenotypes and populations, reducing the need for separate specialized systems and improving scalability without proportionally increasing complexity
3Measurement precision
If expert labor is used for data manipulation, then data quality and accuracy are maintained, but costs increase significantly
Solution Approach 1:
The system creates reproducible phenotype vectors that can be copied and applied across multiple studies and data sets. Once the analytical variable construction logic is established, it can be replicated automatically, maintaining consistent data accuracy without requiring repeated expert intervention for each new analysis
Solution Approach 2:
The patent substitutes human expert judgment with algorithmic decision-making rules encoded in the phenotype definitions and analytical variable constructions. This mechanical system maintains precision through consistent application of predefined criteria while eliminating the variable costs associated with human expert labor
Data Source
AI summary
Cohort definition and selection system for a computer having a memory, a central processing unit and a display, the system including: a cohort definition module to configure the memory according to a phenotype vector. The phenotype vector includes a patient ID to uniquely associate the phenotype vector to a patient, a plurality of demographic dimension fields, each demographic dimension field to describe a respective demographic aspect of the patient, a calculated dimension field to describe a calculated information related to the patient, a plurality of phenotype-based dimension fields, each phenotype-based dimension field to indicate relevance of the respective phenotype-based dimension field to the patient, and a child phenotype vector to recursively define a phenotype-based dimension field, and a cohort selection module to select a set of phenotype vectors that are within a predetermined error from a cohort selection criteria.