TY - JOUR
T1 - Introduction and Comparison of Novel Decentral Learning Schemes with Multiple Data Pools for Privacy-Preserving ECG Classification
AU - Baumgartner, Martin
AU - Veeranki, Sai Pavan Kumar
AU - Hayn, Dieter
AU - Schreier, Günter
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/9
Y1 - 2023/9
N2 - Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue. In this work, 6 different decentralized machine learning algorithms are applied to 12-lead ECG classification and compared to conventional, centralized machine learning. The results show that state-of-the-art federated learning leads to reasonable losses of classification performance compared to a standard, central model (−0.054 AUROC) while providing a significantly higher level of privacy. A proposed weighted variant of federated learning (−0.049 AUROC) and an ensemble (−0.035 AUROC) outperformed the standard federated learning algorithm. Overall, considering multiple metrics, the novel batch-wise sequential learning scheme performed best (−0.036 AUROC to baseline). Although, the technical aspects of implementing them in a real-world application are to be carefully considered, the described algorithms constitute a way forward towards preserving-preserving AI in medicine.
AB - Artificial intelligence and machine learning have led to prominent and spectacular innovations in various scenarios. Application in medicine, however, can be challenging due to privacy concerns and strict legal regulations. Methods that centralize knowledge instead of data could address this issue. In this work, 6 different decentralized machine learning algorithms are applied to 12-lead ECG classification and compared to conventional, centralized machine learning. The results show that state-of-the-art federated learning leads to reasonable losses of classification performance compared to a standard, central model (−0.054 AUROC) while providing a significantly higher level of privacy. A proposed weighted variant of federated learning (−0.049 AUROC) and an ensemble (−0.035 AUROC) outperformed the standard federated learning algorithm. Overall, considering multiple metrics, the novel batch-wise sequential learning scheme performed best (−0.036 AUROC to baseline). Although, the technical aspects of implementing them in a real-world application are to be carefully considered, the described algorithms constitute a way forward towards preserving-preserving AI in medicine.
KW - Decentral learning
KW - Decision-support
KW - Deep learning
KW - Machine learning
KW - Privacy-preserving artificial intelligence
UR - http://www.scopus.com/inward/record.url?scp=85168287869&partnerID=8YFLogxK
U2 - 10.1007/s41666-023-00142-5
DO - 10.1007/s41666-023-00142-5
M3 - Article
AN - SCOPUS:85168287869
SN - 2509-498X
VL - 7
SP - 291
EP - 312
JO - Journal of Healthcare Informatics Research
JF - Journal of Healthcare Informatics Research
IS - 3
ER -