AI Medical Compendium Topic:
Forecasting

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Prediction of sepsis patients using machine learning approach: A meta-analysis.

Computer methods and programs in biomedicine
STUDY OBJECTIVE: Sepsis is a common and major health crisis in hospitals globally. An innovative and feasible tool for predicting sepsis remains elusive. However, early and accurate prediction of sepsis could help physicians with proper treatments an...

Machine-learning prediction of adolescent alcohol use: a cross-study, cross-cultural validation.

Addiction (Abingdon, England)
BACKGROUND AND AIMS: The experience of alcohol use among adolescents is complex, with international differences in age of purchase and individual differences in consumption and consequences. This latter underlines the importance of prediction modelin...

Response prediction of nonlinear hysteretic systems by deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Nonlinear hysteretic systems are common in many engineering problems. The maximum response estimation of a nonlinear hysteretic system under stochastic excitations is an important task for designing and maintaining such systems. Although a nonlinear ...

Artificial intelligence-enabled healthcare delivery.

Journal of the Royal Society of Medicine
In recent years, there has been massive progress in artificial intelligence (AI) with the development of deep neural networks, natural language processing, computer vision and robotics. These techniques are now actively being applied in healthcare wi...

Artificial intelligence and medical imaging 2018: French Radiology Community white paper.

Diagnostic and interventional imaging
The rapid development of information technology and data processing capabilities has led to the creation of new tools known as artificial intelligence (AI). Medical applications of AI are emerging, and the French radiology community felt it was there...

Forecasting the spatiotemporal variability of soil CO emissions in sugarcane areas in southeastern Brazil using artificial neural networks.

Environmental monitoring and assessment
Carbon dioxide (CO) is considered one of the main greenhouse effect gases and contributes significantly to global climate change. In Brazil, the agricultural areas offer an opportunity to mitigate this effect, especially with the sugarcane crop, sinc...

Sequential prediction of quantitative health risk assessment for the fine particulate matter in an underground facility using deep recurrent neural networks.

Ecotoxicology and environmental safety
Particulate matter with aerodynamic diameter less than 2.5 µm (PM) in indoor public spaces such as subway stations, has represented a major public health concern; however, forecasting future sequences of quantitative health risk is an effective metho...