AI-based approach for improving the detection of blood doping in sports

Maxx Richard Rahman; Jacob Bejder; Thomas Christian Bonne; Andreas Breenfeldt Andersen; Jesús Rodríguez Huertas; Reid Aikin; Nikolai Baastrup Nordsborg; Wolfgang Maaß

doi:10.48550/arXiv.2203.00001

AI-based approach for improving the detection of blood doping in sports

Maxx Richard Rahman^*, Jacob Bejder, Thomas Christian Bonne, Andreas Breenfeldt Andersen, Jesús Rodríguez Huertas, Reid Aikin, Nikolai Baastrup Nordsborg, Wolfgang Maaß

^*Corresponding author af dette arbejde

August Krogh Sektionen for Human Fysiologi

Publikation: Working paper › Preprint › Forskning

Abstract

Sports officials around the world are facing incredible challenges due to the unfair means of practices performed by the athletes to improve their performance in the game. It includes the intake of hormonal based drugs or transfusion of blood to increase their strength and the result of their training. However, the current direct test of detection of these cases includes the laboratory-based method, which is limited because of the cost factors, availability of medical experts, etc. This leads us to seek for indirect tests. With the growing interest of Artificial Intelligence in healthcare, it is important to propose an algorithm based on blood parameters to improve decision making. In this paper, we proposed a statistical and machine learning-based approach to identify the presence of doping substance rhEPO in blood samples.

Originalsprog	Engelsk
Udgiver	arxiv.org
Sider	1-7
Antal sider	7
DOI	https://doi.org/10.48550/arXiv.2203.00001
Status	Udgivet - 9 feb. 2022

Bibliografisk note

(Preprint)

Emneord

Det Natur- og Biovidenskabelige Fakultet

Adgang til dokumentet

10.48550/arXiv.2203.00001Licens: CC BY

https://arxiv.org/pdf/2203.00001v1Licens: CC BY

Citationsformater

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AU - Aikin, Reid

AU - Nordsborg, Nikolai Baastrup

AU - Maaß, Wolfgang

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