AIMC Topic: Machine Learning

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Characterising the nationwide burden and predictors of unkept outpatient appointments in the National Health Service in England: A cohort study using a machine learning approach.

PLoS medicine
BACKGROUND: Unkept outpatient hospital appointments cost the National Health Service £1 billion each year. Given the associated costs and morbidity of unkept appointments, this is an issue requiring urgent attention. We aimed to determine rates of un...

A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification.

BioMed research international
A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional bi...

Early Prediction of Sepsis Based on Machine Learning Algorithm.

Computational intelligence and neuroscience
Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, a...

miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

PloS one
Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in t...

Learning policy scheduling for text augmentation.

Neural networks : the official journal of the International Neural Network Society
When training deep learning models, data augmentation is an important technique to improve the performance and alleviate overfitting. In natural language processing (NLP), existing augmentation methods often use fixed strategies. However, it might be...

Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods.

Chemical biology & drug design
The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the β-site. The sequential proteolytic cleavage of APP, first by β-secretase and then by γ-secreta...

Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides.

Medical & biological engineering & computing
The ubiquitous antimicrobial peptides (AMPs), with a broad range of antimicrobial activities, represent a great promise for combating the multi-drug resistant infections. In this study, using a large and diverse set of AMPs (2638) and non-AMPs (3700)...

IDS for Industrial Applications: A Federated Learning Approach with Active Personalization.

Sensors (Basel, Switzerland)
Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring...

Association of TLR 9 gene polymorphisms with remission in patients with rheumatoid arthritis receiving TNF-α inhibitors and development of machine learning models.

Scientific reports
Toll-like receptor (TLR)-4 and TLR9 are known to play important roles in the immune system, and several studies have shown their association with the development of rheumatoid arthritis (RA) and regulation of tumor necrosis factor alpha (TNF-α). Howe...

Predicting bloodstream infection outcome using machine learning.

Scientific reports
Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We de...