The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

ABSTRACT: Automatic detection of cognitive distortions from short written text could support large-scale mental-health screening and digital cognitive-behavioural therapy (CBT). Many recent approaches ...

Background Fear of disease progression (FoP) is a common psychological concern among patients with unruptured intracranial aneurysms (UIAs). However, heterogeneity in FoP and the role of psychological ...

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...

Background Despite global efforts to improve nutrition, young women aged 15–24 years in low-income and middle-income countries (LMICs) face persistent dual burdens of malnutrition, marked by high ...

This project explores the determinants of inflation prediction errors using data from the Survey of Consumer Expectations (SCE) from 2013 to 2023 and monthly National and yearly State-level PCE. The ...

Bayesian Additive Regression Trees (BART) is a nonparametric ensemble method that models complex relationships by summing a collection of decision trees, each operating as a weak learner. The Bayesian ...

Abstract: The classification problem represents a funda-mental challenge in machine learning, with logistic regression serving as a traditional yet widely utilized method across various scientific ...