AIMC Topic: Machine Learning

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Artificial Intelligence in Pathology.

Deutsches Arzteblatt international
BACKGROUND: Increasing digitalization enables the use of artificial intelligence (AI) and machine learning in pathology. However, these technologies have only just begun to be implemented, and no randomized prospective trials have yet shown a benefit...

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

Medicine
In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefo...

How Convolutional Neural Network Architecture Biases Learned Opponency and Color Tuning.

Neural computation
Recent work suggests that changing convolutional neural network (CNN) architecture by introducing a bottleneck in the second layer can yield changes in learned function. To understand this relationship fully requires a way of quantitatively comparing...

Reproducibility in machine learning for health research: Still a ways to go.

Science translational medicine
Machine learning for health must be reproducible to ensure reliable clinical use. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reprodu...

Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.

Proceedings of the National Academy of Sciences of the United States of America
Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SAR...

Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.

Briefings in bioinformatics
Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving...

Biological applications of knowledge graph embedding models.

Briefings in bioinformatics
Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and ...

A novel machine learning approach (svmSomatic) to distinguish somatic and germline mutations using next-generation sequencing data.

Zoological research
Somatic mutations are a large category of genetic variations, which play an essential role in tumorigenesis. Detection of somatic single nucleotide variants (SNVs) could facilitate downstream analysis of tumorigenesis. Many computational methods have...

Predicting pressure injury using nursing assessment phenotypes and machine learning methods.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure in...

The risk of racial bias while tracking influenza-related content on social media using machine learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they have the potential to adversely impact the people they are built to help. In this study, we explore t...