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Machine learning and biological validation identify sphingolipids as potential mediators of paclitaxel-induced neuropathy in cancer patients.

eLife
BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a serious therapy-limiting side effect of commonly used anticancer drugs. Previous studies suggest that lipids may play a role in CIPN. Therefore, the present study aimed to identify th...

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...

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 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...

Exploring Nurses' Behavioural Intention to Adopt AI Technology: The Perspectives of Social Influence, Perceived Job Stress and Human-Machine Trust.

Journal of advanced nursing
AIM: This study examines how social influence, human-machine trust and perceived job stress affect nurses' behavioural intentions towards AI-assisted care technology adoption from a new perspective and framework. It also explores the interrelationshi...

Navigating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions.

Urologic oncology
INTRODUCTION: The objective of this study is to predict the probability of prostate cancer in PI-RADS 3 lesions using machine learning methods that incorporate clinical and mpMRI parameters.

Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images.

Journal of thrombosis and thrombolysis
To explore the predictive value of traditional machine learning (ML) and deep learning (DL) algorithms based on computed tomography pulmonary angiography (CTPA) images for short-term adverse outcomes in patients with acute pulmonary embolism (APE). T...

Attitudes and Perceptions of Australian Dentists and Dental Students Towards Applications of Artificial Intelligence in Dentistry: A Survey.

European journal of dental education : official journal of the Association for Dental Education in Europe
INTRODUCTION: As artificial intelligence (AI) rapidly evolves in dentistry, understanding dentists' and dental students' perspectives is key. This survey evaluated Australian dentists' and students' attitudes and perceptions of AI in dentistry.