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Liu Q, Chen Y, Xie P, Luo Y, Wang B, Meng Y, Zhong J, Mei J, Zou W. Development of a predictive machine learning model for pathogen profiles in patients with secondary immunodeficiency. BMC Med Inform Decis Mak 2024; 24:48. [PMID: 38350899 PMCID: PMC10863296 DOI: 10.1186/s12911-024-02447-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Secondary immunodeficiency can arise from various clinical conditions that include HIV infection, chronic diseases, malignancy and long-term use of immunosuppressives, which makes the suffering patients susceptible to all types of pathogenic infections. Other than HIV infection, the possible pathogen profiles in other aetiology-induced secondary immunodeficiency are largely unknown. METHODS Medical records of the patients with secondary immunodeficiency caused by various aetiologies were collected from the First Affiliated Hospital of Nanchang University, China. Based on these records, models were developed with the machine learning method to predict the potential infectious pathogens that may inflict the patients with secondary immunodeficiency caused by various disease conditions other than HIV infection. RESULTS Several metrics were used to evaluate the models' performance. A consistent conclusion can be drawn from all the metrics that Gradient Boosting Machine had the best performance with the highest accuracy at 91.01%, exceeding other models by 13.48, 7.14, and 4.49% respectively. CONCLUSIONS The models developed in our study enable the prediction of potential infectious pathogens that may affect the patients with secondary immunodeficiency caused by various aetiologies except for HIV infection, which will help clinicians make a timely decision on antibiotic use before microorganism culture results return.
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Affiliation(s)
- Qianning Liu
- School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, Jiangxi, China
| | - Yifan Chen
- School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, Jiangxi, China
| | - Peng Xie
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Ying Luo
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
- Department of Infectious Diseases, Third People's Hospital of Jiujiang, Jiujiang, 332000, Jiangxi, China
| | - Buxuan Wang
- School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, Jiangxi, China
| | - Yuanxi Meng
- The First Clinical Medical College,Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Jiaqian Zhong
- The First Clinical Medical College,Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Jiaqi Mei
- The First Clinical Medical College,Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Wei Zou
- Department of Infectious Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, Jiangxi, China.
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