AIMC Topic: Machine Learning

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Machine learning-empowered sleep staging classification using multi-modality signals.

BMC medical informatics and decision making
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EO...

The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.

BMC infectious diseases
BACKGROUND: Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting...

Automated machine learning model for fundus image classification by health-care professionals with no coding experience.

Scientific reports
To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct da...

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.

Environmental monitoring and assessment
Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the con...

Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data.

Journal of environmental management
Dams and reservoirs have significantly altered river flow dynamics worldwide. Accurately representing reservoir operations in hydrological models is crucial yet challenging. Detailed reservoir operation data is often inaccessible, leading to relying ...

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers.

Environmental science and pollution research international
Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictio...

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Critical care explorations
Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine...

Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.

Expert opinion on drug safety
INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medic...

Skin Conductance-Based Acupoint and Non-Acupoint Recognition Using Machine Learning.

IEEE journal of biomedical and health informatics
Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the ski...

Method for Incomplete and Imbalanced Data Based on Multivariate Imputation by Chained Equations and Ensemble Learning.

IEEE journal of biomedical and health informatics
The classification analysis of incomplete and imbalanced data is still a challenging task since these issues could negatively impact the training of classifiers, which were also found in our study on the physical fitness assessments of patients. And ...