학술논문
A federated AI strategy for the classification of patients with Mucosa Associated Lymphoma Tissue (MALT) lymphoma across multiple harmonized cohorts
Document Type
Conference
Author
Source
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Engineering in Medicine & Biology Society (EMBC), 2021 43rd Annual International Conference of the IEEE. :1666-1669 Nov, 2021
Subject
Language
ISSN
2694-0604
Abstract
Mucosa Associated Lymphoma Tissue (MALT) type is an extremely rare type of lymphoma which occurs in less than 3% of patients with primary Sjögren’s Syndrome (pSS). No reported studies so far have been able to investigate risk factors for MALT development across multiple cohort databases with sufficient statistical power. Here, we present a generalized, federated AI (artificial intelligence) strategy which enables the training of AI algorithms across multiple harmonized databases. A case study is conducted towards the development of MALT classification models across 17 databases on pSS. Advanced AI algorithms were developed, including federated Multinomial Naïve Bayes (FMNB), federated gradient boosting trees (FGBT), FGBT with dropouts (FDART), and the federated Multilayer Perceptron (FMLP). The FDART with dropout rate 0.3 achieved the best performance with sensitivity 0.812, and specificity 0.829, yielding 8 biomarkers as prominent for MALT development.