AIMC Topic: Machine Learning

Clear Filters Showing 3341 to 3350 of 34417 articles

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US.

EBioMedicine
BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these risks post-pandemic have been absent. We aimed to use large electronic health records (EHR) and ML a...

Green finance reform, multifaceted collaborative governance, and corporate greenwashing: Evidence from double/debiased machine learning method.

Journal of environmental management
In order to promote the economic structure's green transformation and identify replicable and generalizable practices in green financial development, the Chinese government has established pilot zones for green finance reform and innovation (PZGFRI) ...

Machine learning-based prediction of the necessity for the surgical treatment of distal radius fractures.

Journal of orthopaedic surgery and research
BACKGROUND: Treatments for distal radius fractures (DRFs) are determined by various factors. Therefore, quantitative or qualitative tools have been introduced to assist in deciding the treatment approach. This study aimed to develop a machine learnin...

Peritoneal cytology predicting distant metastasis in uterine carcinosarcoma: machine learning model development and validation.

World journal of surgical oncology
OBJECTIVE: This study develops and validates a machine learning model using peritoneal cytology to predict distant metastasis in uterine carcinosarcoma, aiding clinical decision-making.

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

Environmental monitoring and assessment
The suspended sediment load (SSL) of a river is a key indicator of water resource management, river morphology, and ecosystem health. This study analyzes historical changes in SSL and evaluates machine learning (ML) models for SSL prediction in the G...

Unveiling Berberine analogues as potential inhibitors of Escherichia coli FtsZ through machine learning molecular docking and molecular dynamics approach.

Scientific reports
The bacterial cell division protein FtsZ, a crucial GTPase, plays a vital role in the formation of the contractile Z-ring, which is essential for bacterial cytokinesis. Consequently, inhibiting FtsZ could prevent the formation of proto-filaments and ...

OPTUNA optimization for predicting chemical respiratory toxicity using ML models.

Journal of computer-aided molecular design
Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly related to medical destiny and human health. This paper presents an enhanced model for chemical respiratory toxicity prediction. It used a combinatio...

Communication-efficient distributed learning with Local Immediate Error Compensation.

Neural networks : the official journal of the International Neural Network Society
Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compressio...

A systematic review of machine learning algorithms for breast cancer detection.

Tissue & cell
Breast cancer is one of the leading causes of death and morbidity among women worldwide. Identifying cancerous cells remains a complex and time-consuming task, particularly when performed manually by radiologists or pathologists, contributing to high...

Interpretable Machine Learning for Predicting Anterior Uveitis in Axial Spondyloarthritis.

Journal of clinical rheumatology : practical reports on rheumatic & musculoskeletal diseases
BACKGROUND: Axial spondyloarthritis (axSpA) is a chronic inflammatory disease primarily affecting the spine and sacroiliac joints, with anterior uveitis (AU) as a common extra-articular manifestation. Predicting AU onset in axSpA patients is challeng...