AIMC Topic: Machine Learning

Clear Filters Showing 6071 to 6080 of 34417 articles

Artificial intelligence in antimicrobial stewardship: a systematic review and meta-analysis of predictive performance and diagnostic accuracy.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology
The increasing threat of antimicrobial resistance has prompted a need for more effective antimicrobial stewardship programs (AMS). Artificial intelligence (AI) and machine learning (ML) tools have emerged as potential solutions to enhance decision-ma...

Machine learning discovery of novel antihypertensive peptides from highland barley protein inhibiting angiotensin I-converting enzyme (ACE).

Food research international (Ottawa, Ont.)
Hypertension is a major global health concern, and there is a need for new antihypertensive agents derived from natural sources. This study aims to identify novel angiotensin I-converting enzyme (ACE) inhibitors from bioactive peptides derived from f...

Learning Pose Controllable Human Reconstruction With Dynamic Implicit Fields From a Single Image.

IEEE transactions on visualization and computer graphics
Recovering a user-special and controllable human model from a single RGB image is a nontrivial challenge. Existing methods usually generate static results with an image consistent subject's pose. Our work aspires to achieve pose-controllable human re...

Semisupervised Contrastive Learning for Bioactivity Prediction Using Cell Painting Image Data.

Journal of chemical information and modeling
Morphological profiling has recently demonstrated remarkable potential for identifying the biological activities of small molecules. Alongside the fully supervised and self-supervised machine learning methods recently proposed for bioactivity predict...

Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.

ACS sensors
Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In this study, we propose an ideal gas sensor design strategy with high screening efficiency and low c...

Machine learning-based identification of animal feeding operations in the United States on a parcel-scale.

The Science of the total environment
The increasing global demand for meat and dairy products, fueled by rapid industrialization, has led to the expansion of Animal Feeding Operations (AFOs) in the United States (US). These operations, often found in clusters, generate large amounts of ...

Differential Diagnosis of Urinary Cancers by Surface-Enhanced Raman Spectroscopy and Machine Learning.

Analytical chemistry
Bladder, kidney, and prostate cancers are prevalent urinary cancers, and developing efficient detection methods is of significance for the early diagnosis of them. However, noninvasive and sensitive detection of urinary cancers still challenges tradi...

Coronary health index based on immunoglobulin light chains to assess coronary heart disease risk with machine learning: a diagnostic trial.

Journal of translational medicine
BACKGROUND: Recent studies suggest a connection between immunoglobulin light chains (IgLCs) and coronary heart disease (CHD). However, current diagnostic methods using peripheral blood IgLCs levels or subtype ratios show limited accuracy for CHD, lac...

Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment.

Journal of translational medicine
INTRODUCTION: Cardiac arrest (CA), characterized by its heterogeneity, poses challenges in patient management. This study aimed to identify clinical subphenotypes in CA patients to aid in patient classification, prognosis assessment, and treatment de...

External validation of AI-based scoring systems in the ICU: a systematic review and meta-analysis.

BMC medical informatics and decision making
BACKGROUND: Machine learning (ML) is increasingly used to predict clinical deterioration in intensive care unit (ICU) patients through scoring systems. Although promising, such algorithms often overfit their training cohort and perform worse at new h...