Study case/patient distribution algorithm. by Navid Behzadi Koochnai (18554931)

Variable importance of features included in the gradient boosting machine learning (XGB) algorithm for predicting postinduction hypotension. by Manabu Yoshimura (11040049)

The illustration depicts our CQ-CNN architecture for binary image classification. by Mominul Islam (3513758)

Presentation_1_Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy.PPTX by Umesh C. Sharma (10785063)

“…Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. …”

The primary composite outcome between the propensity matched Bebtelovimab (BEB) monoclonal antibody (MAb) and untreated control groups. by Srilekha Sridhara (15374444)

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Data_Sheet_1_Optimizing clinical outcomes in polypharmacy through poly-de-prescribing: a longitudinal study.docx by Doron Garfinkel (18465243)

Study cohort selection. by Srilekha Sridhara (15374444)

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Subgroup analysis for the primary composite outcome stratified by patient vaccination status (fully vaccinated vs. not fully vaccinated), age category (age <65 vs. age ≥65), and im... by Srilekha Sridhara (15374444)

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Kaplan Meier curves for the composite outcome in patients who received Bebtelovimab monoclonal antibody treated group vs. not-treated control group between April 5, 2022, and Augus... by Srilekha Sridhara (15374444)

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Multivariable Cox proportional hazard model^b'*' for the composite outcome among the post-propensity matched COVID-19 infected patients in the Banner Healthcare System be... by Srilekha Sridhara (15374444)

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Patient characteristics and covariate balance before and after propensity matching. by Srilekha Sridhara (15374444)

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DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx by Yuhong Huang (115702)

“…We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). …”

Optimal Timings Codebook.xlsx by Emma Spillane (11249121)

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). …”

Optimal Time Intervals of Breech Births Dataset by Emma Spillane (11249121)

Table_1_Data-based modeling for hypoglycemia prediction: Importance, trends, and implications for clinical practice.docx by Liyin Zhang (6371999)

Data_Sheet_1_Prediction-Driven Decision Support for Patients With Mild Stroke: A Model Based on Machine Learning Algorithms.docx by Xinping Lin (1591438)

Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf by Jordan D. Chambers (6732224)

Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)