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Logistic Models

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Interpretable machine learning-based prediction of 28-day mortality in ICU patients with sepsis: a multicenter retrospective study.

Frontiers in cellular and infection microbiology
BACKGROUND: Sepsis is a major cause of mortality in intensive care units (ICUs) and continues to pose a significant global health challenge, with sepsis-related deaths contributing substantially to the overall burden on healthcare systems worldwide. ...

Prediction of surgical necessity in children with ureteropelvic junction obstruction using machine learning.

Irish journal of medical science
BACKGROUND: Hydronephrosis developing at the ureteropelvic junction due to obstruction poses clinical challenges as it has the potential to cause renal damage.

Statistical models versus machine learning approach for competing risks in proctological surgery.

Updates in surgery
Clinical risk prediction models are ubiquitous in many surgical domains. The traditional approach to develop these models involves the use of regression analysis. Machine learning algorithms are gaining in popularity as an alternative approach for pr...

Sexual dimorphism of the humerus bones in a French sample: comparison of several statistical models including machine learning models.

International journal of legal medicine
Sex estimation is an important part of skeletal analysis and forensic identification. Traditionally pelvic traits are utilized for accurate sex estimation. However, the long bones, especially humerus, have been proved to be as effective for determine...

Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

BMC emergency medicine
BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these s...

Interpretable machine learning model for outcome prediction in patients with aneurysmatic subarachnoid hemorrhage.

Critical care (London, England)
BACKGROUND: Aneurysmatic subarachnoid hemorrhage (aSAH) is a critical condition associated with significant mortality rates and complex rehabilitation challenges. Early prediction of functional outcomes is essential for optimizing treatment strategie...

Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.

BMC medical imaging
OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aim...

A machine learning based variable selection algorithm for binary classification of perinatal mortality.

PloS one
The identification of significant predictors with higher model performance is the key objective in classification domain. A machine learning-based variable selection technique termed as CARS-Logistic model is proposed by coupling competitive adaptive...

Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients.

PloS one
PURPOSE: Left atrial thrombus or spontaneous echo contrast (LAT/SEC) are widely recognized as significant contributors to cardiogenic embolism in non-valvular atrial fibrillation (NVAF). This study aimed to construct and validate an interpretable pre...