Public Health & Policy

Environmental Health

Latest AI and machine learning research in environmental health for healthcare professionals.

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ToxMPNN: A deep learning model for small molecule toxicity prediction.

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, ...

Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.

Previous studies have successfully predicted overweight status by applying deep learning to 12-lead ...

Using transfer learning-based plant disease classification and detection for sustainable agriculture.

Subsistence farmers and global food security depend on sufficient food production, which aligns with...

The future of radiology and radiologists: AI is pivotal but not the only change afoot.

Uncertainty regarding the future of radiologists is largely driven by the emergence of artificial in...

Rewiring care delivery through Digital Therapeutics (DTx): a machine learning-enhanced assessment and development (M-LEAD) framework.

BACKGROUND: Digital transformation has sparked profound change in the healthcare sector through the ...

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA).

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and oc...

Addressing bias in artificial intelligence for public health surveillance.

Components of artificial intelligence (AI) for analysing social big data, such as natural language p...

Toxicovigilance 2.0 - modern approaches for the hazard identification and risk assessment of toxicants in human beings: A review.

The attempt to define toxicovigilance can be based on defining its fundamental principles: preventio...

Optimization strategy of community planning for environmental health and public health in smart city under multi-objectives.

As population density increases, environmental hygiene and public health become increasingly severe....

Enhancing transvenous lead extraction risk prediction: Integrating imaging biomarkers into machine learning models.

BACKGROUND: Machine learning (ML) models have been proposed to predict risk related to transvenous l...

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches.

The accurate identification of chemicals with ocular toxicity is of paramount importance in health h...

The principle of uncertainty in biology: Will machine learning/artificial intelligence lead to the end of mechanistic studies?

Molecular Biology has long tried to discover mechanisms, considering that unless we understand the p...

Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more...

Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification.

Early time series classification predicts the class label of a given time series before it is comple...

Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes.

With the advent of the second industrial revolution, mining and metallurgical processes generate lar...

Deep learning in water protection of resources, environment, and ecology: achievement and challenges.

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Ef...

Molecular marker identification, antioxidant, antinociceptive, and anti-inflammatory responsiveness of malonic acid capped silver nanoparticle.

Nano-sized silver has drawn a great deal of attention in the field of health sciences owing to its r...

A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future.

Given the severe effects that toxic compounds present in wastewater streams have on humans, it is im...

A neural network model for the evolution of learning in changing environments.

Learning from past experience is an important adaptation and theoretical models may help to understa...

Artificial Intelligence in Medicine: A Caution About Good Intentions and Where It May Lead.

Implementing Artificial Intelligence in medicine is revolutionizing how medicine is practiced. It ha...

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