Simulated Patient Dataset Generation for Privacy-Safe ML Training
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Solution Overview
Problem
Medical professionals in underserved areas often lack the expertise to accurately diagnose and treat patients due to shortages of qualified medical professionals, and existing systems for analyzing medical data are hindered by privacy and security regulations.
Innovation Solution
A system that generates a simulated patient population dataset based on feature parameters and outcomes, allowing a machine learning engine to be trained for automated medical outcome prediction without using real patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If real patient medical data is used to train machine learning systems, then the quality and quantity of training data is improved, but patient privacy and security concerns arise due to sensitive nature of medical information
Solution Approach 1:
The patent creates synthetic copies of patient medical data through simulation that preserve the statistical properties and patterns of real medical data while containing no actual patient information. The simulated patient population datasets replicate the complexity and variability of real medical records, enabling effective machine learning training without using genuine patient data, thus resolving the contradiction between data quantity/quality and privacy protection
Solution Approach 2:
The patent introduces simulated patient data as an intermediary between real patient data and machine learning training systems. This intermediary layer allows researchers to access and analyze medical data patterns without direct exposure to sensitive patient information, effectively mediating the conflict between data utilization and privacy protection
2Object-affected harmful factors
If medical data is anonymized by removing patient names and identifying information, then some privacy protection is achieved, but the data remains vulnerable to re-identification through physical characteristics and symptoms
Solution Approach 1:
Instead of attempting to anonymize real data, the patent creates entirely synthetic copies that never contained patient identities in the first place. The simulated datasets replicate the medical and demographic characteristics necessary for research while being fundamentally different from any real patient record, making re-identification impossible and providing stronger privacy protection than traditional anonymization
Solution Approach 2:
The patent inverts the traditional approach by not starting with real data and removing identifiers, but rather generating data synthetically from the ground up. This inversion ensures that no patient information exists to be leaked, fundamentally reversing the privacy risk model
3Measurement precision
If qualified medical professionals are deployed to underserved areas, then diagnostic accuracy and treatment quality improve, but the shortage of medical professionals in these regions persists
Solution Approach 1:
The patent replaces the mechanical system of human medical professionals with an automated machine learning-based diagnostic system. This substitution enables diagnostic capabilities to be deployed in underserved areas without requiring specialized medical personnel, overcoming the limitation of professional shortages while maintaining diagnostic functionality through algorithmic analysis of patient data
Solution Approach 2:
The patent creates a digital copy of medical expert knowledge embedded in machine learning models that can be distributed and executed in resource-limited settings. This digital replication of expertise allows advanced diagnostic capabilities to be accessed in underserved areas without physically deploying specialized medical professionals
Data Source
AI summary
A system receives feature parameters, each identifying possible values for one of a set of features. The system receives outcomes corresponding to the feature parameters. The system generates a simulated patient population dataset with multiple simulated patient datasets, each simulated patient dataset associated with the outcomes and including feature values falling within the possible values identified by the feature parameters. The system may train a machine learning engine based on the simulated patient population dataset and optionally additional simulated patient population datasets. The machine learning engine generates predicted outcomes based on the training in response to queries identifying feature values.