ArticleBioinformation2025
Data to diagnosis: Machine learning-aided prediction of bloodstream infections.
Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
7 authors.
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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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.