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Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms.

Scientific reports
Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies ...

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

Scientific reports
The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irreparable complications and even death. Artificial intelligence (AI) techniques like machine learning (ML) and deep learning (DL) greatly aid in accurately...

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.

JMIR aging
BACKGROUND: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily re...

Awareness and Perceptions of ChatGPT Among Academics and Research Professionals in Riyadh, Saudi Arabia: Implications for Responsible AI Use.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Chat Generative Pre-Trained (ChatGPT) Transformer was created by OpenAI and has a powerful tool used in research. This study aimed to assess the awareness and perceptions of ChatGPT among researchers and academicians at King Saud Universit...

Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

BMC urology
OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning.

BMC medicine
BACKGROUND: The advances in deep learning-based pathological image analysis have invoked tremendous insights into cancer prognostication. Still, lack of interpretability remains a significant barrier to clinical application.

Pioneering noninvasive colorectal cancer detection with an AI-enhanced breath volatilomics platform.

Theranostics
The sensitivity and specificity of current breath biomarkers are often inadequate for effective cancer screening, particularly in colorectal cancer (CRC). While a few exhaled biomarkers in CRC exhibit high specificity, they lack the requisite sensit...

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study.

Frontiers in public health
BACKGROUND: Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigor...

Established machine learning models to predict readmission for elderly patients with ischemic heart disease.

Kardiologia polska
BACKGROUND: The contribution of clinical features associated with 30-day or 1-year readmission in elderly patients with ischemic heart disease (IHD) and whether these features can be used to predict the readmission risk of patients has not been studi...

Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features.

Sensors (Basel, Switzerland)
The cognitive state of a person can be categorized using the circumplex model of emotional states, a continuous model of two dimensions: arousal and valence. The purpose of this research is to select a machine learning model(s) to be integrated into ...