Authenticated ML Model Customization for Healthcare Data Security
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Solution Overview
Problem
Traditional machine-learning training techniques in healthcare settings are resource-intensive, time-consuming, and may compromise patient data security, particularly in environments with large electronic health records, and can inadvertently expose protected information to unauthorized users.
Innovation Solution
A computer-implemented method that provides authenticated customizations of machine-learning models by accessing messages with timestamp and user identification data, identifying a training group of data entities, determining a training dataset, modifying pre-trained models based on this dataset, and providing the modified models during an authenticated network session, while ensuring secure access and removing modifications upon session end.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine-learning training techniques are used in healthcare settings, then model customization and performance are improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on general healthcare data before deployment. This allows the models to be quickly adapted to specific providers' data during authenticated sessions without requiring extensive retraining, thus improving efficiency while maintaining performance.
Solution Approach 2:
The training process is segmented into two phases: (1) pre-training on general healthcare data that can be shared across providers, and (2) fine-tuning on provider-specific authenticated data. This segmentation reduces the computational burden on individual providers while maintaining model customization and performance.
2Adaptability or versatility
If traditional machine-learning training techniques are used in healthcare settings, then model customization is improved, but time consumption increases significantly
Solution Approach 1:
Models are pre-trained on general healthcare data in advance, so when a healthcare provider needs a customized model, the system can quickly adapt it during an authenticated session without requiring extensive training time, thus reducing time loss while maintaining customization.
Solution Approach 2:
The system applies local quality by training models on provider-specific data only during authenticated sessions when needed, rather than requiring all providers to undergo lengthy training processes. This allows customization to occur locally and on-demand, reducing time consumption.
3Reliability
If traditional machine-learning training techniques are used in healthcare settings, then model performance is improved, but patient data security may be compromised
Solution Approach 1:
The system introduces an authentication mechanism as an intermediary between the model training process and patient data. The model is trained on authenticated, provider-specific data during controlled sessions, ensuring that only authorized providers can access and train on their patient data, thus maintaining security while achieving performance.
Solution Approach 2:
The system applies local quality by training models on provider-specific authenticated data rather than sharing raw patient data across providers. This ensures that each provider's data remains secure and localized, reducing security risks while maintaining model performance through customization.
4Adaptability or versatility
If traditional machine-learning training techniques are used in healthcare settings, then model customization is improved, but resource intensity increases significantly
Solution Approach 1:
The training process is segmented into pre-training on general data (performed once) and fine-tuning on provider-specific data (performed on-demand during authenticated sessions). This segmentation reduces the computational resource consumption for individual providers while maintaining model customization capability.
Solution Approach 2:
The system performs preliminary pre-training on general healthcare data before deployment, so that when customization is needed, the model can be quickly adapted with minimal computational resources during authenticated sessions, reducing overall resource intensity.
Data Source
AI summary
Techniques are disclosed for providing an authenticated model customization for a machine-learning model. A cloud service provider platform accesses a message including, at least, timestamp data and user identification data. A training group of data entities is identified based on the data in the message. A training dataset is determined based on the training group of data entities. A machine-learning model is modified based on the training dataset. The modified machine-learning model is provided during an authenticated network session associated with the user identification data. In some embodiments, the modification of the machine-learning model is removed based on a determination that the authenticated network session had ended.