Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis

Mateo, J.; Rius-Peris, Juan Manuel; Maraña-Pérez, A.I.; Valiente-Armero, A.; Torres, Ana

doi:10.1016/j.bbe.2021.04.015

Cited by 21 publications

(16 citation statements)

References 41 publications

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“…As a preventive tool, Heaton et al developed an MLA to predict seasonal RSV outbreaks to allow for timely immunoprophylaxis injections for children predisposed to poor infection outcomes ( 18 ). Other studies using MLAs that predict suitable treatment courses ( 19 ) or patient outcomes ( 15 , 20 ) for bronchiolitis patients, a disease commonly caused by RSV, require a proper diagnosis prior to running the algorithm. These RSV preventive and treatment studies do not address the need for broad screening of incoming pediatric patients and rapid identification of RSV infected patients.…”

Section: Discussionmentioning

confidence: 99%

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients

et al. 2022

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Respiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record (EHR) data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.

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Section: Discussionmentioning

confidence: 99%

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients

et al. 2022

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“…As a preventive tool, Heaton et al developed an MLA to predict seasonal RSV outbreaks to allow for timely immunoprophylaxis injections for children predisposed to poor infection outcomes. 18 Other studies using MLAs that predict suitable treatment courses 19 or patient outcomes 15,20 for bronchiolitis patients, a disease commonly caused by RSV, require a proper diagnosis prior to running the algorithm. These RSV preventive and treatment studies do not address the need for broad screening of incoming pediatric patients and rapid identi cation of RSV infected patients.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Early Prediction of Respiratory Syncytial Virus (RSV) in Pediatric Hospitalized Patients

Tso

Lam

Calvert

et al. 2022

Preprint

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Respiratory syncytial virus (RSV) causes millions of infections among children in the US each year and can cause severe disease or death. Infections that are not promptly detected can cause outbreaks that put other hospitalized patients at risk. No tools besides diagnostic testing are available to rapidly and reliably predict RSV infections among hospitalized patients. We conducted a retrospective study from pediatric electronic health record data and built a machine learning model to predict whether a patient will test positive to RSV by nucleic acid amplification test during their stay. Our model demonstrated excellent discrimination with an area under the receiver-operating curve of 0.919, a sensitivity of 0.802, and specificity of 0.876. Our model can help clinicians identify patients who may have RSV infections rapidly and cost-effectively. Successfully integrating this model into routine pediatric inpatient care may assist efforts in patient care and infection control.

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“…The main advantages are that it is fast to run and is scalable and allows parallel computing. [22][23][24][25] XGB algorithms are developed under the framework of gradient boosting. XGB features parallel tree boosting (also known as gradient-boosted decision trees), which solves many data science problems accurately and quickly.…”

Section: Model Developmentmentioning

confidence: 99%

eXtreme Gradient Boosting-based method to classify patients with COVID-19

Ramón

Torres

Milara

et al. 2022

Journal of Investigative Medicine

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Different demographic, clinical and laboratory variables have been related to the severity and mortality following SARS-CoV-2 infection. Most studies applied traditional statistical methods and in some cases combined with a machine learning (ML) method. This is the first study to date to comparatively analyze five ML methods to select the one that most closely predicts mortality in patients admitted with COVID-19. The aim of this single-center observational study is to classify, based on different types of variables, adult patients with COVID-19 at increased risk of mortality. SARS-CoV-2 infection was defined by a positive reverse transcriptase PCR. A total of 203 patients were admitted between March 15 and June 15, 2020 to a tertiary hospital. Data were extracted from the electronic medical record. Four supervised ML algorithms (k-nearest neighbors (KNN), decision tree (DT), Gaussian naïve Bayes (GNB) and support vector machine (SVM)) were compared with the eXtreme Gradient Boosting (XGB) method proposed to have excellent scalability and high running speed, among other qualities. The results indicate that the XGB method has the best prediction accuracy (92%), high precision (>0.92) and high recall (>0.92). The KNN, SVM and DT approaches present moderate prediction accuracy (>80%), moderate recall (>0.80) and moderate precision (>0.80). The GNB algorithm shows relatively low classification performance. The variables with the greatest weight in predicting mortality were C reactive protein, procalcitonin, glutamyl oxaloacetic transaminase, glutamyl pyruvic transaminase, neutrophils, D-dimer, creatinine, lactic acid, ferritin, days of non-invasive ventilation, septic shock and age. Based on these results, XGB is a solid candidate for correct classification of patients with COVID-19.

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Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis

Cited by 21 publications

References 41 publications

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients

Machine learning early prediction of respiratory syncytial virus in pediatric hospitalized patients

Machine Learning Early Prediction of Respiratory Syncytial Virus (RSV) in Pediatric Hospitalized Patients

eXtreme Gradient Boosting-based method to classify patients with COVID-19

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