AI Medical Compendium Topic:
Forecasting

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Attractor Ranked Radial Basis Function Network: A Nonparametric Forecasting Approach for Chaotic Dynamic Systems.

Scientific reports
The curse of dimensionality has long been a hurdle in the analysis of complex data in areas such as computational biology, ecology and econometrics. In this work, we present a forecasting algorithm that exploits the dimensionality of data in a nonpar...

Evolving Role and Future Directions of Natural Language Processing in Gastroenterology.

Digestive diseases and sciences
In line with the current trajectory of healthcare reform, significant emphasis has been placed on improving the utilization of data collected during a clinical encounter. Although the structured fields of electronic health records have provided a con...

Radiomics: from qualitative to quantitative imaging.

The British journal of radiology
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and...

Imaging research in fibrotic lung disease; applying deep learning to unsolved problems.

The Lancet. Respiratory medicine
Over the past decade, there has been a groundswell of research interest in computer-based methods for objectively quantifying fibrotic lung disease on high resolution CT of the chest. In the past 5 years, the arrival of deep learning-based image anal...

Machine learning for predicting cardiac events: what does the future hold?

Expert review of cardiovascular therapy
: With the increase in the number of patients with cardiovascular diseases, better risk-prediction models for cardiovascular events are needed. Statistical-based risk-prediction models for cardiovascular events (CVEs) are available, but they lack the...

Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction.

Artificial intelligence in medicine
Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, ...

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.

European radiology
Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be practiced in the near future, but several issues need to be resolved before AI can be widely implemented in daily practice. These include the role of th...

A Deep Learning Model for Segmentation of Geographic Atrophy to Study Its Long-Term Natural History.

Ophthalmology
PURPOSE: To develop and validate a deep learning model for the automatic segmentation of geographic atrophy (GA) using color fundus images (CFIs) and its application to study the growth rate of GA.

Machine Learning for Cancer Drug Combination.

Clinical pharmacology and therapeutics
When treating multiple complex diseases, such as cancer, polytherapy may demonstrate efficiency than monotherapy. However, due to the multiplicative relationship between the number of drugs and cell lines vs. the number of combinations, it is impract...

Bio-informational futures: The convergence of artificial intelligence and synthetic biology.

EMBO reports
Synthetic biology and artificial intelligence naturally converge in the biofoundry. Navigating the ethical and societal issues of the biofoundry's potential remains a major challenge.