AIMC Topic: Machine Learning

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Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods.

Computers in biology and medicine
BACKGROUND: COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rap...

Visualizing biomarkers and their association with clinical outcomes: A machine learning approach.

Computers in biology and medicine
INTRODUCTION: Clinical investigators often seek to identify biomarkers that are associated with clinical outcomes. The challenge is that biomarkers tend to be numerous, often inter-correlated, and with varying degrees of association with the clinical...

Dual-level diagnostic feature learning with recurrent neural networks for treatment sequence recommendation.

Journal of biomedical informatics
In recent years, the massive electronic medical records (EMRs) have supported the development of intelligent medical services such as treatment recommendations. However, existing treatment recommendations usually follow the traditional sequential rec...

A Bi-level representation learning model for medical visual question answering.

Journal of biomedical informatics
Medical Visual Question Answering (VQA) targets at answering questions related to given medical images and it contains tremendous potential in healthcare services. However, researches on medical VQA are still facing challenges, particularly on how to...

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning.

Sensors (Basel, Switzerland)
Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum...

Application of Convolutional Neural Network in Motor Bearing Fault Diagnosis.

Computational intelligence and neuroscience
In the field of mechanical and electrical equipment, the motor rolling bearing is a workpiece that is extremely prone to damage and failure. However, the traditional fault diagnosis methods cannot keep up with the development pace of the times becaus...

Machine learning and the electrocardiogram over two decades: Time series and meta-analysis of the algorithms, evaluation metrics and applications.

Artificial intelligence in medicine
BACKGROUND: The application of artificial intelligence to interpret the electrocardiogram (ECG) has predominantly included the use of knowledge engineered rule-based algorithms which have become widely used today in clinical practice. However, over r...

Machine Learning and Lexicon Approach to Texts Processing in the Detection of Degrees of Toxicity in Online Discussions.

Sensors (Basel, Switzerland)
This article focuses on the problem of detecting toxicity in online discussions. Toxicity is currently a serious problem when people are largely influenced by opinions on social networks. We offer a solution based on classification models using machi...

Cybercrime: Identification and Prediction Using Machine Learning Techniques.

Computational intelligence and neuroscience
In the world of cyber age, cybercrime is spreading its root extensively. Supervised classification methods such as the support vector machine (SVM) and K-nearest neighbor (KNN) models are employed for the classification of cybercrime data. Likewise, ...

A novel machine learning model for class III surgery decision.

Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie
PURPOSE: The primary purpose of this study was to develop a new machine learning model for the surgery/non-surgery decision in class III patients and evaluate the validity and reliability of this model.