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

Research output: Working paper › Preprint › Research

Standard

AI-based approach for improving the detection of blood doping in sports. / Rahman, Maxx Richard; Bejder, Jacob; Bonne, Thomas Christian; Breenfeldt Andersen, Andreas; Huertas, Jesús Rodríguez; Aikin, Reid; Nordsborg, Nikolai Baastrup; Maaß, Wolfgang.

arxiv.org, 2022. p. 1-7.

Research output: Working paper › Preprint › Research

Harvard

Rahman, MR, Bejder, J, Bonne, TC, Breenfeldt Andersen, A, Huertas, JR, Aikin, R, Nordsborg, NB & Maaß, W 2022 'AI-based approach for improving the detection of blood doping in sports' arxiv.org, pp. 1-7. https://doi.org/10.48550/arXiv.2203.00001

APA

Rahman, M. R., Bejder, J., Bonne, T. C., Breenfeldt Andersen, A., Huertas, J. R., Aikin, R., Nordsborg, N. B., & Maaß, W. (2022). AI-based approach for improving the detection of blood doping in sports. (pp. 1-7). arxiv.org. https://doi.org/10.48550/arXiv.2203.00001

Vancouver

Rahman MR, Bejder J, Bonne TC, Breenfeldt Andersen A, Huertas JR, Aikin R et al. AI-based approach for improving the detection of blood doping in sports. arxiv.org. 2022 Feb 9, p. 1-7. https://doi.org/10.48550/arXiv.2203.00001

Author

Rahman, Maxx Richard ; Bejder, Jacob ; Bonne, Thomas Christian ; Breenfeldt Andersen, Andreas ; Huertas, Jesús Rodríguez ; Aikin, Reid ; Nordsborg, Nikolai Baastrup ; Maaß, Wolfgang. / AI-based approach for improving the detection of blood doping in sports. arxiv.org, 2022. pp. 1-7

Bibtex

@techreport{41cd4fbd60594ecc81de1511d308507e,
title = "AI-based approach for improving the detection of blood doping in sports",
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.",
keywords = "Faculty of Science, Blood doping, Artificial intelligence (AI), Drug abuse, rhEPO, WADA, Sports, Machine learning",
author = "Rahman, {Maxx Richard} and Jacob Bejder and Bonne, {Thomas Christian} and {Breenfeldt Andersen}, Andreas and Huertas, {Jes{\'u}s Rodr{\'i}guez} and Reid Aikin and Nordsborg, {Nikolai Baastrup} and Wolfgang Maa{\ss}",
note = "(Preprint)",
year = "2022",
month = feb,
day = "9",
doi = "10.48550/arXiv.2203.00001",
language = "English",
pages = "1--7",
publisher = "arxiv.org",
type = "WorkingPaper",
institution = "arxiv.org",

}

RIS

TY - UNPB

T1 - AI-based approach for improving the detection of blood doping in sports

AU - Rahman, Maxx Richard

AU - Bejder, Jacob

AU - Bonne, Thomas Christian

AU - Breenfeldt Andersen, Andreas

AU - Huertas, Jesús Rodríguez

AU - Aikin, Reid

AU - Nordsborg, Nikolai Baastrup

AU - Maaß, Wolfgang

N1 - (Preprint)

PY - 2022/2/9

Y1 - 2022/2/9

N2 - 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.

AB - 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.

KW - Faculty of Science

KW - Blood doping

KW - Artificial intelligence (AI)

KW - Drug abuse

KW - rhEPO

KW - WADA

KW - Sports

KW - Machine learning

U2 - 10.48550/arXiv.2203.00001

DO - 10.48550/arXiv.2203.00001

M3 - Preprint

SP - 1

EP - 7

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

PB - arxiv.org

ER -

ID: 328540177