AIMC Topic: Machine Learning

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End-to-end interstitial fibrosis assessment of kidney biopsies with a machine learning-based model.

Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
BACKGROUND: The extent of interstitial fibrosis in the kidney not only correlates with renal function at the time of biopsy but also predicts future renal outcome. However, its assessment by pathologists lacks good agreement. The aim of this study is...

ABEILLE: a novel method for ABerrant Expression Identification empLoying machine LEarning from RNA-sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Current advances in omics technologies are paving the diagnosis of rare diseases proposing a complementary assay to identify the responsible gene. The use of transcriptomic data to identify aberrant gene expression (AGE) has demonstrated ...

Present and future of machine learning in breast surgery: systematic review.

The British journal of surgery
BACKGROUND: Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is si...

Causes of Outcome Learning: a causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome.

International journal of epidemiology
Nearly all diseases are caused by different combinations of exposures. Yet, most epidemiological studies focus on estimating the effect of a single exposure on a health outcome. We present the Causes of Outcome Learning approach (CoOL), which seeks t...

Past, Present, and Future of Machine Learning and Artificial Intelligence for Breast Cancer Screening.

Journal of breast imaging
Breast cancer screening has evolved substantially over the past few decades because of advancements in new image acquisition systems and novel artificial intelligence (AI) algorithms. This review provides a brief overview of the history, current stat...

Speckle classification of a multimode fiber based on Inception V3.

Applied optics
Multimode optical fiber plays an important role in endoscope miniaturization. With the development of deep learning and machine learning, neural networks can be used to identify and classify speckle patterns obtained at the fiber output. Based on the...

Forecasting macroscopic dynamics in adaptive Kuramoto network using reservoir computing.

Chaos (Woodbury, N.Y.)
Forecasting a system's behavior is an essential task encountering the complex systems theory. Machine learning offers supervised algorithms, e.g., recurrent neural networks and reservoir computers that predict the behavior of model systems whose stat...

Global diabetes burden: analysis of regional differences to improve diabetes care.

BMJ open diabetes research & care
INTRODUCTION: The current evaluation processes of the burden of diabetes are incomplete and subject to bias. This study aimed to identify regional differences in the diabetes burden on a universal level from the perspective of people with diabetes.

Development and Validation of a Machine Learning Approach Leveraging Real-World Clinical Narratives as a Predictor of Survival in Advanced Cancer.

JCO clinical cancer informatics
PURPOSE: Predicting short-term mortality in patients with advanced cancer remains challenging. Whether digitalized clinical text can be used to build models to enhance survival prediction in this population is unclear.

Cavernous Malformations and Artificial Intelligence: Machine Learning Applications.

Neurosurgery clinics of North America
Significant progress has been made in the use of artificial intelligence (AI) in clinical medicine over the past decade, but the clinical development of AI faces challenges. Although the spectrum of AI applications is growing within clinical medicine...