AIMC Topic: Machine Learning

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Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study.

Frontiers in immunology
BACKGROUNDS: During the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world's healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) ...

Diabetes prediction model based on GA-XGBoost and stacking ensemble algorithm.

PloS one
Diabetes, as an incurable lifelong chronic disease, has profound and far-reaching effects on patients. Given this, early intervention is particularly crucial, as it can not only significantly improve the prognosis of patients but also provide valuabl...

SCAWA scales: A new digital tool for wrinkles clinical grading based on AI.

International journal of cosmetic science
OBJECTIVE: Clinical assessment of wrinkle depth is essential for efficacy evaluations of anti-ageing products. Standardized photographic scales, representative of different wrinkle depths are often used by experts to assign subjects reliable grades. ...

Optimal layer selection for latent data augmentation.

Neural networks : the official journal of the International Neural Network Society
While data augmentation (DA) is generally applied to input data, several studies have reported that applying DA to hidden layers in neural networks, i.e., feature augmentation, can improve performance. However, in previous studies, the layers to whic...

Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

Computer methods and programs in biomedicine
BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the wait...

Machine-learning-based classification of obstructive sleep apnea using 19-channel sleep EEG data.

Sleep medicine
OBJECTIVE: This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including p...

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects.

Bioresource technology
This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML ca...

Muscle Fat and Volume Differences in People With Hip-Related Pain Compared With Controls: A Machine Learning Approach.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Hip-related pain (HRP) affects young to middle-aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunc...

Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency.

Artificial intelligence in medicine
BACKGROUND & OBJECTIVES: Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and targeted...

Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature.

Sensors (Basel, Switzerland)
The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key article...