The proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence whi...
Artificial intelligence (AI) can be a powerful tool for data analysis, but it can also mislead investigators, due in part to a fundamental difference between classic data analysis and data analysis using AI. A more or less limited data set is analyze...
Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches...
Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent...
Imputing data is a critical issue for machine learning practitioners, including in the life sciences domain, where missing clinical data is a typical situation and the reliability of the imputation is of great importance. Currently, there is no canon...
Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet,...
Handling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across variou...
BACKGROUND: Over the course of a clinical trial, irregularities may arise in the data. Trialists implement human-intensive, expensive central statistical monitoring procedures to identify and correct these irregularities before the results of the tri...
Artificial intelligence (AI) models often face performance drops after deployment to external datasets. This study evaluated the potential of a novel data augmentation framework based on generative adversarial networks (GANs) that creates synthetic p...
OBJECTIVES: To assess the accuracy of ChatGPT-4 Omni (GPT-4o) in biomedical statistics. The recent novel inauguration of Artificial Intelligence ChatGPT-Omni (GPT-4o), has emerged with the potential to analyze sophisticated and extensive data sets, c...