Federated learning for validating machine learning models trained in different hospital networks.

• Grant number: 22/16683-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: March 01, 2023
• End date: January 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Agreement: European Commission (Horizon 2020)
• Principal Investigator: Paulo Mazzoncini de Azevedo Marques
• Grantee: Gilson Yuuji Shimizu
• Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Associated research grant: 21/06137-4 - Predicting Cardiovascular Events Using Machine Learning., AP.R

Abstract

While several machine learning (ML) models that use electronic health record (EHR) data have been developed in recent years, there is a great need for external validation of these models. Prediction models may perform well in a center with a population similar to the training data but may perform worse in centers with different patient characteristics. To develop generalizable models and discriminate equally well between different cohorts, multicenter studies should be considered. However, these multicenter studies are often limited if they require sharing patient data in a centralized location. Even if data is anonymized before being transferred, there is always some risk of anonymity being compromised on certain types of data. Approaches based on federated learning circumvent these limitations by sharing models and metrics instead of data, making it possible to improve the generalizability of prediction models while safeguarding patient privacy. This project proposes implementing a federated learning model and its evaluation as a tool to optimize the generalization capacity of machine learning models in different hospital networks.

Scientific publications

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

SHIMIZU, GILSON YUUJI; SCHREMPF, MICHAEL; ROMAO, ELEN ALMEIDA; JAUK, STEFANIE; KRAMER, DIETHER; RAINER, PETER P.; CARDEAL DA COSTA, JOSE ABRAO; DE AZEVEDO-MARQUES, JOAO MAZZONCINI; SCARPELINI, SANDRO; SUZUKI, KATIA MITIKO FIRMINO; et al. Machine learning-based risk prediction for major adverse cardiovascular events in a Brazilian hospital: Development, external validation, and interpretability. PLoS One, v. 19, n. 10, p. 23-pg., 2024-10-11. (21/06137-4, 22/16683-9, 23/01695-4)

SHIMIZU, GILSON YUUJI; ROMAO, ELEN ALMEIDA; CARDEAL DA COSTA, JOSE ABRAO; MAZZONCINI DE AZEVEDO-MARQUES, JOAO; SCARPELINI, SANDRO; FIRMINO SUZUKI, KATIA MITIKO; CESAR, HILTON VICENTE; AZEVEDO-MARQUES, PAULO M.. External validation and interpretability of machine learning-based risk prediction for major adverse cardiovascular events. 2024 IEEE 37TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS, CBMS 2024, v. N/A, p. 6-pg., 2024-01-01. (14/50889-7, 21/06137-4, 22/16683-9, 23/01695-4)