AIMC Topic: Machine Learning

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Machine learning algorithm as a diagnostic tool for hypoadrenocorticism in dogs.

Domestic animal endocrinology
Canine hypoadrenocorticism (CHA) is a life-threatening condition that affects approximately 3 of 1,000 dogs. It has a wide array of clinical signs and is known to mimic other disease processes, including kidney and gastrointestinal diseases, creating...

Double Dipping in Machine Learning: Problems and Solutions.

Biological psychiatry. Cognitive neuroscience and neuroimaging

Building more accurate decision trees with the additive tree.

Proceedings of the National Academy of Sciences of the United States of America
The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learn...

NeuRiPP: Neural network identification of RiPP precursor peptides.

Scientific reports
Significant progress has been made in the past few years on the computational identification of biosynthetic gene clusters (BGCs) that encode ribosomally synthesized and post-translationally modified peptides (RiPPs). This is done by identifying both...

Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.

Circulation. Cardiovascular imaging
BACKGROUND: Echocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volume...

A Db-Scan Binarization Algorithm Applied to Matrix Covering Problems.

Computational intelligence and neuroscience
The integration of machine learning techniques and metaheuristic algorithms is an area of interest due to the great potential for applications. In particular, using these hybrid techniques to solve combinatorial optimization problems (COPs) to improv...

A zero-shot learning approach to the development of brain-computer interfaces for image retrieval.

PloS one
Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which ...

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers.

IEEE journal of biomedical and health informatics
In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are star...