AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Forecasting

Showing 231 to 240 of 1493 articles

Clear Filters

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.

BMC bioinformatics
BACKGROUND: Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the info...

Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques.

Environmental research
Real-time flood forecasting is one of the most pivotal measures for flood management, and real-time error correction is a critical step to guarantee the reliability of forecasting results. However, it is still challenging to develop a robust error co...

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

Journal of medical imaging and radiation sciences
Uncertainty regarding the future of radiologists is largely driven by the emergence of artificial intelligence (AI). If AI succeeds, will radiologists continue to monopolize imaging services? As AI accuracy progresses with alacrity, radiology reads w...

TCDformer: A transformer framework for non-stationary time series forecasting based on trend and change-point detection.

Neural networks : the official journal of the International Neural Network Society
Although time series prediction models based on Transformer architecture have achieved significant advances, concerns have arisen regarding their performance with non-stationary real-world data. Traditional methods often use stabilization techniques ...

Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

PloS one
BACKGROUND: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series appr...

Deep learning applications for kidney histology analysis.

Current opinion in nephrology and hypertension
PURPOSE OF REVIEW: Nephropathology is increasingly incorporating computational methods to enhance research and diagnostic accuracy. The widespread adoption of digital pathology, coupled with advancements in deep learning, will likely transform our pa...

Precision forecasting of spray-dry desulfurization using Gaussian noise data augmentation and k-fold cross-validation optimized neural computing.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Perceptron models have become integral tools for pattern recognition and classification problems in engineering fields. This study envisioned implementing artificial neural networks to forecast the performance of a mini-spray dryer for desulfurizatio...

[Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?].

Herzschrittmachertherapie & Elektrophysiologie
The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understan...

Which model is more efficient in carbon emission prediction research? A comparative study of deep learning models, machine learning models, and econometric models.

Environmental science and pollution research international
Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econo...