ArticleScientific reports2021
Predicting bloodstream infection outcome using machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 37 citations in OpenAlex.
- Medical Artificial Intelligence: Opportunities and Challenges In Infectious Disease Management.Current medicinal chemistry · 2026Review
- Early prediction of bloodstream infections in ICU patients using machine learning methods based on routine laboratory parameters.BMC infectious diseases · 2025Article
- Prediction of bloodstream infection using machine learning based primarily on biochemical data.Scientific reports · 2025Article
- Development and application of an early prediction model for risk of bloodstream infection based on real-world study.BMC medical informatics and decision making · 2025Article
- Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection.NPJ digital medicine · 2025Article
- Early diagnosis and prognostic prediction of secondary bloodstream infections caused byFrontiers in cellular and infection microbiology · 2025Article
- Machine learning in infectious diseases: potential applications and limitations.Annals of medicine · 2024Review
- Clinical prediction for surgical versus nonsurgical interventions in patients with vertebral osteomyelitis and discitis.Journal of spine surgery (Hong Kong) · 2024Article
- Prediction of carbapenem-resistant gram-negative bacterial bloodstream infection in intensive care unit based on machine learning.BMC medical informatics and decision making · 2024Article
- A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Identifying the need for infection-related consultations in intensive care patients using machine learning models.Scientific reports · 2024Article
- A machine-learning model for prediction of Acinetobacter baumannii hospital acquired infection.PloS one · 2024Article
- Characterization of the Pathogen Distribution and Drug Resistance in Bloodstream Infections During COVID-19 Pandemic in a Tertiary Hospital in Eastern China: Comparison with the Pre-Pandemic Period.Infection and drug resistance · 2024Article
- Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2023Review
- Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder.International journal of molecular sciences · 2023Article
- Predicting Antibiotic Resistance in ICUs Patients by Applying Machine Learning in Vietnam.Infection and drug resistance · 2023Article
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- Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data.Proceedings of machine learning research · 2021Article
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.
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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.