Predicting bloodstream infection outcome using machine learning

Zoabi, Yazeed; Kehat, Orli; Lahav, Dan; Weiss-Meilik, Ahuva; Adler, Amos; Shomron, Noam

doi:10.1038/s41598-021-99105-2

Cited by 18 publications

(20 citation statements)

References 51 publications

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“…Therefore, it is important to obtain a more accurate predictive model for mortality and the decision-making process of the model must be understood by the physician. A recent study developed an ML model to predict patient outcomes of BSI based on electronic medical records and the model AUC was 0.81 using only 25 features ( Zoabi et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: Predicting fulminant sepsis by interpretable machine learning

Chen²,

Zheng³

2022

Front. Microbiol.

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BackgroundAcinetobacter baumannii complex-caused bloodstream infection (ABCBSI) is a potentially fatal infection in intensive care units (ICUs). This study proposed an interpretable machine learning (ML) model to predict ABCBSI fulminant fatality.MethodsA retrospective study of ICU patients with ABCBSI was performed in China from 2009 to 2020. Patients were stratified into two groups: those that suffered from fulminant sepsis and died within 48 h, and those that survived for more than 48 h. The clinical score systems and ML models with Shapley additive explanation (SHAP) were used to develop the prediction models. The ML model was internally validated with five-fold cross-validation, and its performance was assessed using seven typical evaluation indices. The top 20 features ranked by the SHAP scores were also calculated.ResultsAmong 188 ICU patients with ABCBSI, 53 were assigned to the non-survival group and 135 to the survival group. The XGBoost model exhibited the greatest area under the receiver operating characteristic curve (AUC), which outperformed other models (logistic regression, AUC = 0.914; support vector machine, AUC = 0.895; random forest, AUC = 0.972; and naive Bayesian, AUC = 0.908) and clinical scores (Acute Physiology and Chronic Health Evaluation II (APACHE II), AUC = 0.855; Sequential Organ Failure Assessment (SOFA), AUC = 0.837). It also had a sensitivity of 0.868, a specificity of 0.970, an accuracy of 0.941, a positive predictive value of 0.920, a negative predictive value of 0.949, and an F1 score of 0.893. As well as identifying the top 12 different important predictors that contribute to early mortality, it also assessed their quantitative contribution and noteworthy thresholds.ConclusionBased on the XGBoost model, early mortality in ABCBSI is estimated to be more reliable than other models and clinical scores. The 12 most important features with corresponding thresholds were identified and more importantly, the SHAP method can be used to interpret this predictive model and support individual patient treatment strategies.

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Section: Discussionmentioning

confidence: 99%

Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: Predicting fulminant sepsis by interpretable machine learning

Chen²,

Zheng³

2022

Front. Microbiol.

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“…This predictive capability is especially valuable in clinical settings where rapid decision-making is critical [55]. By predicting BSIs accurately, ML models can facilitate earlier intervention strategies, potentially leading to improved patient outcomes and reduced mortality rates [56]. Furthermore, the integration of ML into hospital systems offers a pathway to more streamlined resource allocation [57][58][59].…”

Section: Clinical Relevance and Model Performancementioning

confidence: 99%

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Bopche,

Damås

2024

Preprint

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Abstract. Purpose: Bloodstream infections (BSIs) present significant public health challenges. With the advent of machine learning (ML), promising predictive models have been developed. This study evaluates their performance through a systematic review and meta-analysis. Methods: We performed a comprehensive systematic review across multiple databases, including PubMed, IEEE Xplore, ScienceDirect, ACM Digital Library, SpringerLink, Web of Science, Scopus, and Google Scholar. Eligible studies focused on BSIs within any hospital setting, employing ML models as the diagnostic test. We evaluated the risk of bias with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist and assessed the quality of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. Models reporting the area under the receiver operating characteristic curve (AUROC) were included in the meta-analysis to identify key performance drivers. Results: After screening, a total of 30 studies were eligible for synthesis, from which 41 models and 8 data types were extracted. Most of the studies were carried out in the inpatient settings (n=17; 56%), followed by the emergency department (ED) settings (n=7; 23%), and followed by the ICU settings (n=6; 20%). The reported AUROCs in the hospital inpatients settings, ranged from 0.51-0.866, in the ICU settings AUROCs ranged from 0.668-0.970, and in the emergency department (ED) settings the AUROCs of the models ranged from 0.728-0.844. One study reported prospective cohort study, while two prospectively validated their models. In the meta-analysis, laboratory tests, Complete Blood Count/Differential Count (CBC/DC), and ML model type contributed the most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual ML models can accurately predict BSIs at different stages of patient trajectory. Although they enable early prediction of BSI, a comprehensive approach to integrate data types and models is necessary. Systematic reporting, externally validated, and clinical implementation studies are needed to establish clinical confidence.

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“…The main objective of this model was to identify the poor outcomes of patients receiving hospital treatment for the first time. This model was developed based on the data collected from EMR between 2014 and 2020 [ 90 ]. Numerous studies were conducted to identify patients related to sepsis-like infections, but BSI-based research models are very limited, and this is a research gap that needs to be considered to develop more models that could be directly helpful for healthcare during pandemic situations, since BSI is directly connected to COVID-19 patients.…”

Section: Omicronmentioning

confidence: 99%

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

et al. 2023

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

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Predicting bloodstream infection outcome using machine learning

Cited by 18 publications

References 51 publications

Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: Predicting fulminant sepsis by interpretable machine learning

Acinetobacter baumannii complex-caused bloodstream infection in ICU during a 12-year period: Predicting fulminant sepsis by interpretable machine learning

Recent Advancements in Machine Learning-Based Bloodstream Infection Prediction: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

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