Digital Twin Data Completion via Statistical Aggregation
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Solution Overview
Problem
Existing digital twin systems for humans are incomplete due to missing data, limiting their effectiveness in clinical studies and medical decision-making.
Innovation Solution
A system that creates a digital twin of an individual by incorporating existing data and supplementing missing data entries with statistically relevant medical data from multiple individuals, ensuring the digital twin is comprehensive and accurate for clinical studies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital twin systems use only individual patient data, then data privacy and ethical constraints are maintained, but the digital twin becomes incomplete and lacks statistical significance for clinical studies
Solution Approach 1:
The patent segments the data aggregation process into two distinct layers: individual patient data remains isolated and private, while only aggregated statistical patterns are extracted and used to complete the digital twin. This segmentation allows the system to maintain individual data privacy while still achieving statistical significance through population-level patterns.
Solution Approach 2:
The patent introduces an intermediary aggregation layer that acts as a mediator between individual patient data and the digital twin completion process. This intermediary transforms raw individual data into statistical patterns and distributions, which then serve to supplement the digital twin without exposing or requiring access to individual patient records.
2Reliability
If digital twins are created with incomplete data, then data privacy is protected, but the effectiveness in clinical studies and medical decision-making is limited
Solution Approach 1:
The patent performs preliminary aggregation of statistical patterns from population data before using them to complete the digital twin. This preliminary action prepares the statistical foundations (distributions, correlations, and patterns) in advance, which are then applied to fill missing data entries in the digital twin, ensuring both completeness and statistical validity.
3Reliability
If statistical data from multiple individuals is used to supplement digital twins, then data completeness and statistical significance are achieved, but data aggregation complexity increases
Solution Approach 1:
The patent creates simplified statistical copies (distributions and patterns) of population data that can be directly applied to complete the digital twin. Instead of working with complex individual records, the system uses aggregated statistical representations that capture the essential variability and relationships, significantly reducing aggregation complexity while maintaining statistical significance.
Data Source
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AI summary
The invention relates to a system 15 for creating a digital twin 22 of an individual 16 using data, comprising a data evaluation module 17 which is connected to at least one data storage medium 19 for data exchange, wherein the data evaluation module is designed to carry out the following steps when creating the digital twin 22: Providing a template 20 for a digital twin of a defined individual; Defining a data completion criterion 21 for the digital twin of the defined individual; Recording data of the defined individual in the digital twin; Analyzing the recorded data of the defined individual in the digital twin; Comparing the analyzed data with the defined data completion criterion and defining missing data entries for the digital twin based on the data completion criterion;Selecting data from statistical medical data of multiple individuals 23 from the data storage medium; Supplementing the missing data entries in the digital twin of the defined individual with the selected data. Furthermore, the invention comprises a computer-implemented method.