AI Medical Compendium Topic

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Utilizing data sampling techniques on algorithmic fairness for customer churn prediction with data imbalance problems.

F1000Research
Customer churn prediction (CCP) refers to detecting which customers are likely to cancel the services provided by a service provider, for example, internet services. The class imbalance problem (CIP) in machine learning occurs when there is a huge d...

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications.

Journal of healthcare engineering
The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identifie...

Machine learning-based risk prediction of malignant arrhythmia in hospitalized patients with heart failure.

ESC heart failure
AIMS: Predicting the risk of malignant arrhythmias (MA) in hospitalized patients with heart failure (HF) is challenging. Machine learning (ML) can handle a large volume of complex data more effectively than traditional statistical methods. This study...

Identifying individuals with recent COVID-19 through voice classification using deep learning.

Scientific reports
Recently deep learning has attained a breakthrough in model accuracy for the classification of images due mainly to convolutional neural networks. In the present study, we attempted to investigate the presence of subclinical voice feature alteration ...

Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.

Journal of medical virology
COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood test indicators on the first day of admission for the 422 patients diagnosed with COVID-19 (from Janu...

Automatic detection of actionable radiology reports using bidirectional encoder representations from transformers.

BMC medical informatics and decision making
BACKGROUND: It is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However...

Predicting mortality among patients with liver cirrhosis in electronic health records with machine learning.

PloS one
OBJECTIVE: Liver cirrhosis is a leading cause of death and effects millions of people in the United States. Early mortality prediction among patients with cirrhosis might give healthcare providers more opportunity to effectively treat the condition. ...

Sociodemographic risk factors of under-five stunting in Bangladesh: Assessing the role of interactions using a machine learning method.

PloS one
This paper aims to demonstrate the importance of studying interactions among various sociodemographic risk factors of childhood stunting in Bangladesh with the help of an interpretable machine learning method. Data used for the analyses are extracted...

Using machine learning and big data to explore the drug resistance landscape in HIV.

PLoS computational biology
Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive...

Using machine learning to improve survival prediction after heart transplantation.

Journal of cardiac surgery
BACKGROUND: This study investigates the use of modern machine learning (ML) techniques to improve prediction of survival after orthotopic heart transplantation (OHT).