Federated Learning Model for Clinical Trial Site Prediction
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Solution Overview
Problem
The challenge in site selection for clinical trials is the inability to effectively train machine learning models on decentralized or siloed data due to privacy and proprietary concerns, limiting the availability of historical clinical trial data for improving algorithm performance.
Innovation Solution
Implementing federated learning models that allow for training on decentralized data without sharing, using a federated network where each party trains a model on their own data and exchanges model weights and pointers securely, enabling a privacy-preserving approach to predict clinical trial site performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is centralized in one location for training machine learning models, then algorithm performance is improved, but privacy and proprietary concerns are violated
Solution Approach 1:
The patent segments the training process into distributed components where each data holder (site) trains local model instances on their own data. The global model is formed by aggregating these segmented local models through federated averaging, allowing performance improvement from multiple datasets without centralizing the actual data, thus resolving the contradiction between algorithm performance and privacy protection.
Solution Approach 2:
The patent introduces a federated learning server as an intermediary that coordinates the training process without accessing the actual training data. This server facilitates model weight exchanges and aggregations between participants, enabling collaborative model training while maintaining data privacy through secure multi-party computation protocols.
2Reliability
If additional data from multiple sources is used for training, then model performance is improved, but data sharing restrictions prevent access to external data
Solution Approach 1:
The training data remains segmented at each participating site rather than being consolidated. Each site contributes to the global model through local training and weight sharing, eliminating the need for data sharing while still leveraging multiple data sources to improve model performance through federated aggregation.
Solution Approach 2:
The patent uses model weight copies instead of actual data copies. Each participant receives and trains on copies of the global model weights, contributes updated weights back to the federation, and never shares the underlying training data. This copying mechanism enables multi-source training without data sharing restrictions.
3Reliability
If data is siloed by external vendors for proprietary reasons, then data security is maintained, but model training on external data becomes restricted
Solution Approach 1:
The patent maintains data security by keeping data siloed at each vendor's location while enabling model training across these silos. The segmentation of both data and training processes allows each vendor to maintain control over their data while contributing to a versatile global model through federated learning.
Solution Approach 2:
The federated learning framework provides a universal platform that works across multiple data silos and proprietary systems. It enables the global model to become versatile by learning from diverse data sources without requiring any single vendor to open their data, thus maintaining security while enhancing adaptability.
Data Source
AI summary
Disclosed herein are methods for predicting performance of one or more clinical trial sites for a prospective clinical trial, including obtaining input values of a plurality of clinical operation data associated with the one or more clinical trial sites; and generating predicted quantitative values informative of the performance of the one or more clinical trial sites by applying a trained federated learning model to the plurality of clinical operation data. The trained federated learning model is trained using a federated network. The federated network renders inaccessible a first dataset of a first party to the second party, and further renders inaccessible a second dataset of the second party to the first party.