ArticleJournal of clinical medicine2019
Early Detection of Bacteraemia Using Ten Clinical Variables with an Artificial Neural Network Approach.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Clinical observation of efficacy in adhesive intestinal obstruction with Integrated Chinese (Chaihu Shugan San, ) and Western Medicine: a randomized controlled trial.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Trial
- Intuitive Risk Equation for Post-Transplant Bloodstream Infection Prediction: A Symbolic Regression Approach.Biomedicines · 2026Article
- Artificial Intelligence model to predict resistances in Gram-negative bloodstream infections.NPJ digital medicine · 2025Article
- Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.PLOS digital health · 2024Article
- A Machine Learning Predictive Model of Bloodstream Infection in Hospitalized Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty.Scientific reports · 2023Article
- Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model.European journal of medical research · 2022Article
- Prediction of Bacteremia Based on 12-Year Medical Data Using a Machine Learning Approach: Effect of Medical Data by Extraction Time.Diagnostics (Basel, Switzerland) · 2022Article
- A Data-Driven Framework for Identifying Intensive Care Unit Admissions Colonized With Multidrug-Resistant Organisms.Frontiers in public health · 2022Article
- Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.BMC medical informatics and decision making · 2021Article
- Diagnosing hospital bacteraemia in the framework of predictive, preventive and personalised medicine using electronic health records and machine learning classifiers.The EPMA journal · 2021Article
- An Artificial Intelligence Approach to Bloodstream Infections Prediction.Journal of clinical medicine · 2021Article
- An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.BioMed research international · 2021Article
- Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study.BMC medical research methodology · 2020Article
- Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.Journal of medical Internet research · 2020Article
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Authors and funding
8 authors.
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Abstract
An adequate model for predicting bacteraemia has not yet been developed. This study aimed to evaluate the performance of an artificial neural network (ANN)-based prediction model in comparison with previous statistical models. The performance of multi-layer perceptron (MLP), a representative ANN model, was verified via comparison with a non-neural network model. A total of 1260 bacteraemia episodes were identified in 13,402 patients. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUC) of the prediction performance was 0.729 (95% confidence interval [CI]; 0.712-0.728), while in MLP with 256 hidden layer nodes, it was 0.727 (95% CI; 0.713-0.727). In a conventional Bayesian statistical method, the AUC was 0.7. The aforementioned two MLP models exhibited the highest sensitivity (0.810). The ranking of clinical variables was used to describe the influential power of the prediction. Serum alkaline phosphatase was one of the most influential clinical variables, and one-out search was the best ranking method for measuring the influence of the clinical variables. Furthermore, adding variables beyond the 10 top-ranking ones did not significantly affect the prediction of bacteraemia. The ANN model is not inferior to conventional statistical approaches. Bacteraemia can be predicted using only the top 10 clinical variables determined by a ranking method, and the model can be used in clinical practice by applying real-time monitoring.
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