AIMC Topic: Machine Learning

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Machine learning-based cluster analysis identifies four unique phenotypes of patients with degenerative cervical myelopathy with distinct clinical profiles and long-term functional and neurological outcomes.

EBioMedicine
BACKGROUND: Degenerative cervical myelopathy (DCM), the predominant cause of spinal cord dysfunction among adults, exhibits diverse interrelated symptoms and significant heterogeneity in clinical presentation. This study sought to use machine learnin...

Artificial Intelligence in Urology: Application of a Machine Learning Model to Predict the Risk of Urolithiasis in a General Population.

Journal of endourology
This research presents our application of artificial intelligence (AI) in predicting urolithiasis risk. Previous applications, including AI for stone disease, have focused on stone composition and aiding diagnostic imaging. AI applications centered a...

Screening of Secretory Proteins Linking Major Depressive Disorder with Heart Failure Based on Comprehensive Bioinformatics Analysis and Machine Learning.

Biomolecules
BACKGROUND: Major depressive disorder (MDD) plays a crucial role in the occurrence of heart failure (HF). This investigation was undertaken to explore the possible mechanism of MDD's involvement in HF pathogenesis and identify candidate biomarkers fo...

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Sensors (Basel, Switzerland)
Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide expe...

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

BMC public health
BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) system...

Modified osprey algorithm for optimizing capsule neural network in leukemia image recognition.

Scientific reports
The diagnosis of leukemia is a serious matter that requires immediate and accurate attention. This research presents a revolutionary method for diagnosing leukemia using a Capsule Neural Network (CapsNet) with an optimized design. CapsNet is a cuttin...

Neuroimaging and natural language processing-based classification of suicidal thoughts in major depressive disorder.

Translational psychiatry
Suicide is a growing public health problem around the world. The most important risk factor for suicide is underlying psychiatric illness, especially depression. Detailed classification of suicide in patients with depression can greatly enhance perso...

A critical systematic review on spectral-based soil nutrient prediction using machine learning.

Environmental monitoring and assessment
The United Nations (UN) emphasizes the pivotal role of sustainable agriculture in addressing persistent starvation and working towards zero hunger by 2030 through global development. Intensive agricultural practices have adversely impacted soil quali...

Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.

Frontiers in immunology
PURPOSE: Osteoarthritis (OA) stands as the most prevalent joint disorder. Mitochondrial dysfunction has been linked to the pathogenesis of OA. The main goal of this study is to uncover the pivotal role of mitochondria in the mechanisms driving OA dev...

Identifying Key Training Load and Intensity Indicators in Ice Hockey Using Unsupervised Machine Learning.

Research quarterly for exercise and sport
To identify key training load (TL) and intensity indicators in ice hockey, practice, and game data were collected using a wearable 200-Hz accelerometer and heart rate (HR) recording throughout a four-week (29 days) competitive period (23 practice ses...