Scientific Research Publishing

Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data ()

de Filippis, R. and Al Foysal, A. (2026) Cross-Population Transfer Learning for Antidepressant Treatment Response Prediction: A SHAP-Based Explainability Approach Using Synthetic Multi-Ethnic Data.
BMJ Open

Development and evaluation of a diagnostic aiding tool for differentiating tropical fevers using artificial intelligence approach: a study protocol from tertiary care hospital ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...
IEEE

Gender Classification Based on Machine Learning Models

Abstract: Automatic gender categorization has become an increasingly significant area in computer vision, with several applications. Gender equality is nowadays a keyword for today's scenario. This ...
Frontiers

VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...
AOL

New Doc Explores Gender Identity and Legacy, Including 'Ambiguous' Singer and 1960s Trans Model Who Was Outed (Exclusive)

Premiering on June 24, Enigma spotlights Amanda Lear, the late April Ashley and more, who “contested their identities as women in the court of public opinion," per the film's description In an ...
Frontiers

A machine learning model for robust prediction of sepsis-induced coagulopathy in critically ill patients with sepsis

Introduction: Sepsis-induced coagulopathy (SIC) is a common disease in patients with sepsis. It denotes higher mortality rates and a poorer prognosis in these patients. This study aimed to develop a ...
BMJ

Development of an interpretable machine learning model for frailty risk prediction in older adult care institutions: a mixed-methods, cross-sectional study in China

Objective To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced ...