BMJ

Development and validation of a machine learning-based prediction model for frailty in older adults with diabetes: a study protocol for a retrospective cohort study

Introduction Frailty is a common condition in older adults with diabetes, which significantly increases the risk of adverse health outcomes. Early identification of frailty in this population is ...
IEEE

Learning Software Overtime Estimation From Experts‘ Annotations: A Greedy Cross-Validation-Based Machine Learning Approach

Abstract: Effective overtime planning in software projects remains a critical challenge due to the complex interplay between project constraints and human-centric decision-making. This study ...
IEEE

Phase Formation in Vacuum Arc Melted High Entropy Alloys: Insights From Machine Learning and Experimental Validation

Abstract: In recent years, High Entropy Alloys (HEAs) have gained significant interest due to their unique properties such as high strength, wear resistance, and high temperature stability. However, ...
C&EN

Discovery of Potential Tyrosinase Inhibitors via Machine Learning and Molecular Docking with Experimental Validation of Activity and Skin Permeation

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Natural product libraries represent an ideal source for ...
Hosted on MSN

Which Cross-Validation Method Should You Use in Machine Learning?

Choosing the right cross-validation technique is crucial for building reliable machine learning models. In this video, we explore popular methods like k-fold, stratified k-fold, leave-one-out, and ...
Frontiers

Development and validation of an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity: a cross-sectional study

Objective: To develop and validate an explainable machine learning model for predicting the risk of sleep disorders in older adults with multimorbidity. Methods: A total of 471 older adults with ...
BMJ

Machine learning-based diagnostic model for neonatal intestinal diseases in multiple centres: a cross-sectional study protocol

Background Neonatal intestinal diseases often have an insidious onset and can lead to poor outcomes if not identified early. Early assessment of abnormal bowel function is critical for timely ...
jnis.bmj

Development and validation of an explainable machine learning prediction model for futile recanalization after mechanical thrombectomy in acute large vessel occlusion stroke

Background Mechanical thrombectomy (MT) is the primary treatment for acute ischemic stroke (AIS) caused by large vessel occlusion (LVO). However, the likelihood of futile recanalization (FR) at 90 ...
Frontiers

Machine learning-based spirometry reference values for the Iranian population: a cross-sectional study from the Shahedieh PERSIAN cohort

Objective: This study aimed to determine spirometric norm values for the healthy Iranian adult population and compare them with established norm equations, specifically the GLI-Caucasian and Iranian ...
Digital Trends

Building the ultimate AI and machine learning PC

One of the best ways to reduce your vulnerability to data theft or privacy invasions when using large language model artificial intelligence or machine learning, is to run the model locally. Depending ...
Yahoo

AI vs. Machine Learning: The Key Differences and Why They Matter

Artificial intelligence (AI) is a broad term used to describe various types of virtual "intelligence" designed to replicate aspects of human cognitive abilities. Machine learning (ML) is a type of AI, ...
Scientific Research Publishing

Watanabe, S. (2010) Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. The Journal of Machine Learning ...

ABSTRACT: Geographical variations in all-cause mortality rates may be influenced by residents’ place of residence and the time period under study. Understanding these variations is essential for ...