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Assessing the transportability of clinical prediction models for cognitive impairment using causal models
BackgroundMachine learning models promise to support diagnostic predictions, but may not perform well in new settings. Selecting the best model for a...
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Learning patient-level prediction models across multiple healthcare databases: evaluation of ensembles for increasing model transportability
BackgroundPrognostic models that are accurate could help aid medical decision making. Large observational databases often contain temporal medical...
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Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection
BackgroundImbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Despite...
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An ensemble machine learning approach to predict postoperative mortality in older patients undergoing emergency surgery
BackgroundPrediction of preoperative frailty risk in the emergency setting is a challenging issue because preoperative evaluation cannot be done...
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Transferability and interpretability of the sepsis prediction models in the intensive care unit
BackgroundWe aimed to develop an early warning system for real-time sepsis prediction in the ICU by machine learning methods, with tools for...
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Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review
BackgroundDescribe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology.
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Prediction of lymphedema occurrence in patients with breast cancer using the optimized combination of ensemble learning algorithm and feature selection
BackgroundBreast cancer-related lymphedema is one of the most important complications that adversely affect patients' quality of life. Lymphedema can...
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Automated classification of clinical trial eligibility criteria text based on ensemble learning and metric learning
BackgroundEligibility criteria are the primary strategy for screening the target participants of a clinical trial. Automated classification of...
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Machine learning models for 180-day mortality prediction of patients with advanced cancer using patient-reported symptom data
PurposeThe objective of the current study was to develop and test the performances of different ML algorithms which were trained using...
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Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm
BackgroundMore than 115,000 maternal deaths and 591,000 prenatal deaths occurred in the world per year with anemia, the reduction of red blood cells...
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Development and evaluation of uncertainty quantifying machine learning models to predict piperacillin plasma concentrations in critically ill patients
BackgroundBeta-lactam antimicrobial concentrations are frequently suboptimal in critically ill patients. Population pharmacokinetic (PopPK) modeling...
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The ensemble learning model is not better than the Asian modified CKD-EPI equation for glomerular filtration rate estimation in Chinese CKD patients in the external validation study
ObjectiveTo assess the clinical practicability of the ensemble learning model established by Liu et al. in estimating glomerular filtration rate...
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Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage
BackgroundOutliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply...
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Prior ensemble learning
PurposeCompressed sensing (CS) reduces the measurement time of magnetic resonance (MR) imaging, where the use of regularizers or image priors are key...
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Clinical prediction models and the multiverse of madness
BackgroundEach year, thousands of clinical prediction models are developed to make predictions (e.g. estimated risk) to inform individual diagnosis...
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Ensemble bootstrap methodology for forecasting dynamic growth processes using differential equations: application to epidemic outbreaks
BackgroundEnsemble modeling aims to boost the forecasting performance by systematically integrating the predictive accuracy across individual models....
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Decision curve analysis confirms higher clinical utility of multi-domain versus single-domain prediction models in patients with open abdomen treatment for peritonitis
BackgroundPrediction modelling increasingly becomes an important risk assessment tool in perioperative systems approaches, e.g. in complex patients...
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Machine learning-based mortality prediction models for smoker COVID-19 patients
BackgroundThe large number of SARS-Cov-2 cases during the COVID-19 global pandemic has burdened healthcare systems and created a shortage of...
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Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches
IntroductionOpen globe injuries (OGI) represent a main preventable reason for blindness and visual impairment, particularly in develo** countries....
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An ensemble recruited by α2a-adrenergic receptors is engaged in a stressor-specific manner in mice
α 2a -adrenergic receptor (α 2a -AR) agonists are candidate substance use disorder therapeutics due to their ability to recruit noradrenergic...
