Literature Database Entry

pal2025machine

Saswati Pal, Jorge Torres Gómez, Lisa Y. Debus, Regine Wendt, Florian-Lennert Adrian Lau, Cyrus Khandanpour, Malte Sieren, Stefan Fischer and Falko Dressler, "Machine Learning-Driven Localization of Infection Sources in the Human Cardiovascular System," Proceedings of 9th Workshop on Molecular Communications (WMC 2025), Catania, Italy, April 2025. (to appear)

Abstract

In vivo localization of infection sources is essential for effective diagnosis and targeted disease treatment. In this work, we leverage machine learning models to associate the temporal dynamics of biomarkers as detected at static gateway positions with different infection source locations. In particular, we introduce a simulation module that models infection sources, the release of biomarkers, and their decay as they flow through the bloodstream. From this, we extract time-series biomarker data with varying decay rates to capture temporal patterns from different infection sources at specific gateway positions. We then train a stacked ensemble model using LightGBM and BernoulliNB to analyze biomarker time-series data for classification. Our results reveal that an overall decay percentage of 72.45 % over 500 s enables the ensemble model to perform successfully, achieving a validation accuracy of up to 95.83 %. However, for a higher decay percentage of 94.58 % over 500 s the observed performance of the model decrease. The rapid degradation limits the number of biomarkers counted at the gateways. This effect is more significant for biomarkers released from the kidneys, as they are farther from the gateways located at the two wrists, resulting in a reduced accuracy of 58.33 %.

Quick access

BibTeX

Contact

Saswati Pal
Jorge Torres Gómez
Lisa Y. Debus
Regine Wendt
Florian-Lennert Adrian Lau
Cyrus Khandanpour
Malte Sieren
Stefan Fischer
Falko Dressler

BibTeX reference

Copyright notice

Links to final or draft versions of papers are presented here to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or distributed for commercial purposes without the explicit permission of the copyright holder.

The following applies to all papers listed above that have IEEE copyrights: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

The following applies to all papers listed above that are in submission to IEEE conference/workshop proceedings or journals: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

The following applies to all papers listed above that have ACM copyrights: ACM COPYRIGHT NOTICE. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept., ACM, Inc., fax +1 (212) 869-0481, or permissions@acm.org.

The following applies to all SpringerLink papers listed above that have Springer Science+Business Media copyrights: The original publication is available at www.springerlink.com.

This page was automatically generated using BibDB and bib2web.