AIMC Topic: Machine Learning

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Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques.

Physical and engineering sciences in medicine
An approach is proposed for the detection of chronic heart disorders from the electrocardiogram (ECG) signals. It utilizes an intelligent event-driven ECG signal acquisition system to achieve a real-time compression and effective signal processing an...

Machine learning, the kidney, and genotype-phenotype analysis.

Kidney international
With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcrip...

Multi-view projected clustering with graph learning.

Neural networks : the official journal of the International Neural Network Society
Graph based multi-view learning is well known due to its effectiveness and good clustering performance. However, most existing methods directly construct graph from original high-dimensional data which always contain redundancy, noise and outlying en...

LEAP: Using machine learning to support variant classification in a clinical setting.

Human mutation
Advances in genome sequencing have led to a tremendous increase in the discovery of novel missense variants, but evidence for determining clinical significance can be limited or conflicting. Here, we present Learning from Evidence to Assess Pathogeni...

Meta-Analysis Based on Nonconvex Regularization.

Scientific reports
The widespread applications of high-throughput sequencing technology have produced a large number of publicly available gene expression datasets. However, due to the gene expression datasets have the characteristics of small sample size, high dimensi...

In Silico Prediction of Metabolic Epoxidation for Drug-like Molecules via Machine Learning Methods.

Molecular informatics
Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxi...

Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling.

International journal of medical informatics
BACKGROUND: Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence (through real-time monitoring) and providing options for early intervention be...

Improve Aggressive Driver Recognition Using Collision Surrogate Measurement and Imbalanced Class Boosting.

International journal of environmental research and public health
Real-time recognition of risky driving behavior and aggressive drivers is a promising research domain, thanks to powerful machine learning algorithms and the big data provided by in-vehicle and roadside sensors. However, since the occurrence of aggre...

Data-Driven Modeling of the Bicalutamide Dissolution from Powder Systems.

AAPS PharmSciTech
Low solubility of active pharmaceutical compounds (APIs) remains an important challenge in dosage form development process. In the manuscript, empirical models were developed and analyzed in order to predict dissolution of bicalutamide (BCL) from sol...

Comparison and development of machine learning tools for the prediction of chronic obstructive pulmonary disease in the Chinese population.

Journal of translational medicine
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a major public health problem and cause of mortality worldwide. However, COPD in the early stage is usually not recognized and diagnosed. It is necessary to establish a risk model to predict...