AIMC Topic: Machine Learning

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A hybrid framework for glaucoma detection through federated machine learning and deep learning models.

BMC medical informatics and decision making
BACKGROUND: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (...

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data.

BMC medical informatics and decision making
BACKGROUND: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little ...

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BMC infectious diseases
BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings.

Machine learning for predicting elective fertility preservation outcomes.

Scientific reports
This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019-2022 were inc...

Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Models (CREMLS).

Journal of medical Internet research
The number of papers presenting machine learning (ML) models that are being submitted to and published in the Journal of Medical Internet Research and other JMIR Publications journals has steadily increased. Editors and peer reviewers involved in the...

Machine learning-based potential loss assessment of maize and rice production due to flash flood in Himachal Pradesh, India.

Environmental monitoring and assessment
Flash floods in mountainous regions like the Himalayas are considered to be common natural calamities. Their consequences often are more dangerous than any flood event in the plains. These hazards not only put human lives at threat but also cause eco...

Enhancing short-term streamflow prediction in the Haihe River Basin through integrated machine learning with Lasso.

Water science and technology : a journal of the International Association on Water Pollution Research
With the widespread application of machine learning in various fields, enhancing its accuracy in hydrological forecasting has become a focal point of interest for hydrologists. This study, set against the backdrop of the Haihe River Basin, focuses on...

Precision in Prevention: Tailoring Single-Use Negative Pressure Wound Therapy Utilization Through Artificial Intelligence-Based Surgical Site Complications Risk and Cost Modeling.

Surgical infections
Surgical site complications (SSCs) are common, yet preventable hospital-acquired conditions. Single-use negative pressure wound therapy (sNPWT) has been shown to be effective in reducing rates of these complications. In the era of value-based care, ...

Amplitude-Time Dual-View Fused EEG Temporal Feature Learning for Automatic Sleep Staging.

IEEE transactions on neural networks and learning systems
Electroencephalogram (EEG) plays an important role in studying brain function and human cognitive performance, and the recognition of EEG signals is vital to develop an automatic sleep staging system. However, due to the complex nonstationary charact...

Machine learning prediction model for postoperative ileus following colorectal surgery.

ANZ journal of surgery
BACKGROUND: Postoperative ileus (POI) continues to be a major cause of morbidity following colorectal surgery. Despite best efforts, the incidence of POI in colorectal surgery remains high (~30%). This study aimed to investigate machine learning tech...