Distributed ML Dataset Selection via Metadata Variation
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Solution Overview
Problem
The challenge in developing personalized medical models through machine learning is obtaining sufficient patient data, as data from single hospitals is insufficient for global applications, and combining data from multiple clinical sites is hindered by ethical, legal, and administrative barriers, leading to delayed model deployment and suboptimal performance.
Innovation Solution
A distributed machine learning process, specifically a method where a central server coordinates the training of models using metadata from multiple clinical sites to select and combine updates, ensuring a varied and representative training dataset without transferring data, thus improving model generalization and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from multiple clinical sites is combined to improve model performance, then model generalization capability is improved, but data sharing barriers and ethical/legal constraints worsen
Solution Approach 1:
The patent segments the data processing task by keeping raw patient data localized at each clinical site while extracting and sharing only aggregated statistics and model updates. This segmentation allows model training to benefit from diverse data across multiple sites without violating data sharing constraints, as the actual patient data never leaves its original location.
Solution Approach 2:
The patent introduces a central coordination server as an intermediary that facilitates collaborative model training. This server receives model updates from multiple clinical sites, aggregates them, and coordinates the training process. The intermediary enables data-driven model improvement while maintaining data privacy and ethical constraints by never directly accessing or transferring raw patient data between sites.
2Object-affected harmful factors
If distributed machine learning is used to maintain data privacy, then patient privacy is protected, but data selection and coordination complexity increases
Solution Approach 1:
The patent implements self-service mechanisms where each clinical site autonomously selects and processes its local data according to pre-established criteria. The system automatically evaluates data characteristics and coordinates training without requiring manual intervention or complex centralized control, thereby reducing coordination complexity while maintaining privacy.
Solution Approach 2:
The patent changes the parameters of data sharing from raw data transfer to sharing aggregated statistics and model updates. This parameter transformation simplifies the coordination process, as the central server only needs to aggregate and combine these simplified parameters rather than managing complex data transfer protocols between multiple sites.
3Measurement precision
If sufficient training data is collected from diverse populations, then model accuracy is improved, but time for data collection and deployment increases
Solution Approach 1:
The patent performs preliminary actions by pre-establishing data selection criteria and coordination protocols before the actual model training begins. Clinical sites can quickly contribute their data according to these pre-set guidelines, significantly reducing the time required for data collection and model deployment while ensuring diverse and accurate training data.
Data Source
AI summary
According to an aspect, there is provided a computer implemented method in a central sewer of selecting a training dataset with which to train a model using a distributed machine learning process, wherein the training dataset is to comprise medical data that satisfies one or more clinical requirements and wherein training data in the training dataset is located at a plurality of clinical sites. The method comprises requesting (302) from each of the clinical sites, metadata describing features of matching data at the respective clinical site that satisfies the one or more clinical requirements. The method then comprises determining (304), from the metadata, a measure of variation of the features of the matching data. Based on the measure of variation, the method then comprises selecting (306) training data for the training dataset from the matching data using the metadata.