@inproceedings{3188639,
    title = "Utilizing Incremental Learning for the Prediction of Disease Outcomes
Across Distributed Clinical Data: A Framework and a Case Study",
    author = "Pezoulas, Vasileios C. and Exarchos, Themis P. and Kourou, Konstantina and D. and Tzioufas, Athanasios G. and De Vita, Salvatore and Fotiadis, I, and Dimitrios",
    year = "2020",
    pages = "823-831",
    publisher = "SPRINGER INTERNATIONAL PUBLISHING AG",
    booktitle = "XV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND
COMPUTING - MEDICON 2019",
    doi = "10.1007/978-3-030-31635-8_98",
    keywords = "Predictive modeling; Supervised learning; Incremental learning;
Distributed environments",
    abstract = "In this work, we highlight the need of a supervised learning framework
for disease predictive modeling across distributed clinical data to
overcome the privacy limitations that are introduced by centralized
analysis. Towards this direction, a computational framework is proposed,
consisting of six incremental learning algorithms that are based on
Stochastic Gradient Descent, Naive Bayes, and Gradient Boosting Trees,
to provide new insight on the construction of supervised learning models
across clinical data that are stored in multiple locations. The
applicability of the proposed framework is demonstrated through a
preliminary case study, where a distributed lymphoma prediction model is
constructed across private cloud spaces that consist of clinical data
from patients that have been diagnosed with primary Sjogren’s Syndrome
(pSS). Our results reveal the dominance of the Gradient Boosting Trees,
yielding an average accuracy 91.6% and sensitivity 87.5% towards the
correct identification of lymphoma cases."
}