Data to diagnosis: Machine learning-aided prediction of bloodstream infections

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Title	Data to diagnosis: Machine learning-aided prediction of bloodstream infections
Authors	Mallika Sengupta¹, Sunitaj Parvin¹, Aditya Kundu¹, Shiv Sekhar Chatterjee¹, Ujjala Ghoshal¹, Sayantan Banerjee¹ & Kaushik Mukhopadhyay^2,*
Affiliation	¹Department of Microbiology, All India Institute of Medical Sciences Kalyani, West Bengal, India; ²Department of Pharmacology, All India Institute of Medical Sciences Kalyani, West Bengal, India; *Corresponding author
Email	Mallika Sengupta - E-mail: mallika.micro@aiimskalyani.edu.in Sunitaj Parvin - E-mail: sunitaj.micro_acjr24@aiimskalyani.edu.in Aditya Kundu - E-mail: aditya094agt@gmail.com Shiv Sekhar Chatterjee - E-mail: shivsekhar.micro@aiimskalyani.edu.in Ujjala Ghoshal - E-mail: ujjala.micro@aiimskalyani.edu.in Sayantan Banerjee - E-mail: sayantan.micro@aiimskalyani.edu.in Kaushik Mukhopadhyay - E-mail: kaushik.pharm@aiimskalyani.edu.in; Phone: +91 9163167014
Article Type	Research Article
Date	Received August 1, 2025; Revised August 31, 2025; Accepted August 31, 2025, Published August 31, 2025
Abstract	The application of machine learning (ML) algorithms to routine hematological parameters for early prediction of bloodstream infections and to characterize the microbial and antimicrobial resistance (AMR) profile of culture-positive cases is of interest. A retrospective observational study conducted at AIIMS Kalyani between January 2023 and December 2024, in which blood culture results, AST profiles and 16 routine hematological parameters were collected. ML models including Logistic Regression, Decision Tree, Random Forest and SVM were developed using Python. ROC-AUC, accuracy, sensitivity and specificity were computed for model evaluation. Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus and Pseudomonas aeruginosa were the predominant isolates, with high resistance to cephalosporins and beta-lactam-beta-lactamase inhibitor combinations. The Random Forest model showed the highest predictive power (accuracy: 87%, AUC: 0.70) and key predictors included neutrophil count, CRP, and TLC. The ML models offer promising support for early prediction of BSIs and, when coupled with continuous AMR surveillance, can facilitate rapid diagnosis and guide empirical antibiotic therapy, especially in low-resource settings.
Keywords	Bloodstream infection, machine learning, antimicrobial resistance, prediction models
Citation	Sengupta et al. Bioinformation 21(8): 2282-2287 (2025)
Edited by	P Kangueane
ISSN	0973-2063
Publisher	Biomedical Informatics
License	This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.