TY - GEN
T1 - XGBoost Prediction of Infection of Leukemia Patients with Fever of Unknown Origin
AU - Li, Yan
AU - Song, Yanhui
AU - Ma, Fei
N1 - Funding Information:
This study was supported in part by XJTLU laboratory for intelligent computation and financial technology through XJTLU Key Programme Special Fund (KSF-P-02 and KSF-E-21).
Publisher Copyright:
© 2022 ACM.
PY - 2022/8/19
Y1 - 2022/8/19
N2 - Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients.
AB - Discovering the source of a patient's fever without clinically localised signs can be a daunting task for doctors. In particular for leukaemia patients with fever of unknown origin, fast discovering the source of the fever is a formidable challenge, as this population has the potential to lead to fever in many different situations. In this paper, we applied XGBoost algorithm to predict the pathogenic infections from a big data repository of leukemia patients with fever of unknown origin (FUO) and compared the performance with other machine learning algorithms. Our results illustrates that those machine learning algorithms achieves good performance. In particular, the XGBoost obtains the best performance with an area under receiving-operating-characteristics curve (AUC) of 0.8376 and F1-score of 0.7034. Compared with existing literature, our experiment provides new insights for doctors to determine the cause of fever in leukemia patients.
KW - Fever of unknown origin
KW - Infection prediction
KW - Medical big data
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85144326830&partnerID=8YFLogxK
U2 - 10.1145/3563737.3563761
DO - 10.1145/3563737.3563761
M3 - Conference Proceeding
AN - SCOPUS:85144326830
T3 - ACM International Conference Proceeding Series
SP - 85
EP - 89
BT - ICBIP 2022 - 2022 7th International Conference on Biomedical Signal and Image Processing
PB - Association for Computing Machinery
T2 - 7th International Conference on Biomedical Signal and Image Processing, ICBIP 2022
Y2 - 19 August 2022 through 21 August 2022
ER -