Description
Using electronic health data to predict adverse drug reaction (ADR) incurs practical challenges, such as lack of adequate data from any single site for rare ADR detection, resource constraints on integrating data from multiple sources, and privacy concerns with creating a centralized database from person-specific, sensitive data. We introduce a federated learning framework that can learn a global ADR prediction model from distributed health data held locally at different sites. We propose two novel methods of local model aggregation to improve the predictive capability of the global model. Through comprehensive experimental evaluation using real-world health data from 1 million patients, we demonstrate the effectiveness of our proposed approach in achieving comparable performance to centralized learning and outperforming localized learning models for two types of ADRs. We also demonstrate that, for varying data distributions, our aggregation methods outperform state-of-the-art techniques, in terms of precision, recall, and accuracy.
Learning Objective: After participating in this session, the learner should be better able to:
(1) understand the current challenges associated with centralized and localized learning models for ADR prediction using electronic health data
(2) learn how federated learning can be applied to mitigate the challenges associated with centralized and localized learning models
(3) learn about novel aggregation methods that can improve the predictive capability of ADR detection models in a federated setup
Authors:
Olivia Choudhury (Presenter)
IBM Research
Yoonyoung Park, IBM Research
Theodoros Salonidis, IBM Research
Aris Gkoulalas-Divanis, IBM
Issa Sylla, IBM Research
Amar Das, IBM Research
Presentation Materials: