Secur-e-Health Project: Towards Federated Learning for Smart Pediatric Care

Autores da FMUP

• Tiago Salgado De Magalhães Taveira Gomes

  Autor

• José Alberto Da Silva Freitas

  Autor

Participantes de fora da FMUP

• Rb-Silva, Rita
• Ribeiro, Xavier
• Almeida, Francisca
• Ameijeiras-Rodriguez, Carolina
• Souza, Julio
• Conceicao, Luis
• Marreiros, Goreti

Unidades de investigação

• Laboratório Associado RISE- Rede de Investigação em Saúde

  

  Investigador

  Fernando Carlos De Landér Schmitt

• Medicina da Comunidade, Informação e Decisão em Saúde

• RISE-Health

  

  Investigador

  Fernando Carlos De Landér Schmitt

Abstract

The application of machine learning (ML) algorithms to electronic health records (EHR) data allows the achievement of data-driven insights on various clinical problems and the development of clinical decision support (CDS) systems to improve patient care. However, data governance and privacy barriers hinder the use of data from multiple sources, especially in the medical field due to the sensitivity of data. Federated learning (FL) is an attractive data privacy-preserving solution in this context by enabling the training of ML models with data from multiple sources without any data sharing, using distributed remotely hosted datasets. The Secur-e-Health project aims at developing a solution in terms of CDS tools encompassing FL predictive models and recommendation systems. This tool may be especially useful in Pediatrics due to the increasing demands on Pediatric services, and the current scarcity of ML applications in this field compared to adult care. Herein we provide a description of the technical solution proposed in this project for three specific pediatric clinical problems: childhood obesity management, pilonidal cyst post-surgical care and retinography imaging analysis.

Keywords

• federated learning; machine learning; privacy-preserving protocols; clinical decision support; pediatrics