Literature Database Entry

torres-gomez2021machine


Jorge Torres Gómez, Anke Kuestner, Ketki Pitke, Jennifer Simonjan, Bige Deniz Unluturk and Falko Dressler, "A Machine Learning Approach for Abnormality Detection in Blood Vessels via Mobile Nanosensors," Proceedings of 19th ACM Conference on Embedded Networked Sensor Systems (SenSys 2021), 2nd ACM International Workshop on Nanoscale Computing, Communication, and Applications (NanoCoCoA 2021), Coimbra, Portugal, November 2021, pp. 596–602.


Abstract

Early detection of diseases in the human body is of utmost importance for the diagnosis and medical treatment of patients. Supported by recent advancements in nanotechnology, diseases may be detected by patrolling nanosensors, even before symptoms appear. This paper explores the detection capabilities of nanosensors flowing through the human circulatory system (HCS). We model the HCS through a Markov chain and propose the use of machine learning (ML) methods to learn the corresponding transition probabilities. Doing so, we propose a methodology to develop an early detection mechanism of quorum sensing (QS) molecules released by bacteria. Simulation results indicate the suitability of our machine learning approach as a basis for in-body precision medicine.

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Jorge Torres Gómez
Anke Kuestner
Ketki Pitke
Jennifer Simonjan
Bige Deniz Unluturk
Falko Dressler

BibTeX reference

@inproceedings{torres-gomez2021machine,
    author = {Torres G{\'{o}}mez, Jorge and Kuestner, Anke and Pitke, Ketki and Simonjan, Jennifer and Unluturk, Bige Deniz and Dressler, Falko},
    doi = {10.1145/3485730.3494037},
    title = {{A Machine Learning Approach for Abnormality Detection in Blood Vessels via Mobile Nanosensors}},
    pages = {596--602},
    publisher = {ACM},
    address = {Coimbra, Portugal},
    booktitle = {19th ACM Conference on Embedded Networked Sensor Systems (SenSys 2021), 2nd ACM International Workshop on Nanoscale Computing, Communication, and Applications (NanoCoCoA 2021)},
    month = {11},
    year = {2021},
   }
   
   

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Last modified: 2024-12-04