Abstract
Research in medicine has to deal with the growing amount of data about patients which are made available by modern technologies. All these data might be used to support statistical studies, and for identifying causal relations. To use these data which are spread across hospitals efficient merging techniques, as well as policies to deal with this sensitive information, are strongly needed. In this paper we introduce and empirically test a distributed learning approach, to train Support Vector Machines (SVM), that allows to overcome problems related to privacy and data being spread around. The introduced technique allows to train algorithms without sharing any patients-related information, ensuring privacy and avoids the development of merging tools. We tested this approach on a large dataset and we described results, in terms of convergence and performance; we also provide considerations about the features of an IT architecture designed to support distributed learning computations.
Original language | English |
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Title of host publication | Artificial Intelligence in Medicine |
Subtitle of host publication | 15th Conference on Artificial Intelligence in Medicine, AIME 2015, Pavia, Italy, June 17-20, 2015. Proceedings |
Editors | John H. Holmes, Riccardo Bellazzi, Lucia Sacchi, Niels Peek |
Publisher | Springer, Cham |
Pages | 65-75 |
Number of pages | 11 |
Volume | 9105 |
ISBN (Electronic) | 9783319195513 |
ISBN (Print) | 9783319195506 |
DOIs | |
Publication status | Published - 2015 |
Event | 15th Conference on Artificial Intelligence in Medicine - Pavia, Italy Duration: 17 Jun 2015 → 20 Jun 2015 Conference number: 15 http://aime15.aimedicine.info/ (Link to Conference Website) |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer International |
Volume | 9105 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 15th Conference on Artificial Intelligence in Medicine |
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Abbreviated title | AIME 2015 |
Country/Territory | Italy |
City | Pavia |
Period | 17/06/15 → 20/06/15 |
Internet address |
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