Infectious Disease

Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States.

This study examines the role that racial residential segregation has played in shaping the spread of COVID-19 in the United States as of September 30, 2020. The analysis focuses on the effects of racial residential segregation on mortality and infection rates for the overall population and on racial and ethnic mortality gaps. To account for potential confounding, I assemble a dataset that includes...

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset.

OBJECTIVE: The lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.

Artificial intelligence enabled preliminary diagnosis for COVID-19 from voice cues and questionnaires.

The COVID-19 outbreak was announced as a global pandemic by the World Health Organization in March 2020 and has affected a growing number of people in...

Using machine learning to investigate the public's emotional responses to work from home during the COVID-19 pandemic.

According to event system theory (EST; Morgeson et al., Academy of Management Review, 40, 2015, 515-537), the coronavirus disease 2019 (COVID-19) pand...

Exploring the Influential Factors of Consumers' Willingness Toward Using COVID-19 Related Chatbots: An Empirical Study.

BACKGROUND: Consumers' willingness to use health chatbots can eventually determine if the adoption of health chatbots will succeed in delivering healt...

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We ...

Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.

The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection rate, hospitalizations, and mortality. Many believe ...

Development of a predictive model for retention in HIV care using natural language processing of clinical notes.

OBJECTIVE: Adherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, howeve...

An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature.

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literatu...

m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.

N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and...

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated ge...

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models.

BACKGROUND: Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB ca...

Prediction of treatment failure of tuberculosis using support vector machine with genetic algorithm.

BACKGROUND: Tuberculosis (TB) is a disease that mainly affects human lungs. It can be fatal if the treatment is delayed. This study investigates the p...

A Deep Neural Network to Distinguish COVID-19 from other Chest Diseases Using X-ray Images.

BACKGROUND: Scanning a patient's lungs to detect Coronavirus 2019 (COVID-19) may lead to similar imaging of other chest diseases. Thus, a multidiscipl...

Critical Review of Data Analytics Techniques used in the Expanded Program on Immunization (EPI).

BACKGROUND: Immunization is a significant public health intervention to reduce child mortality and morbidity. However, its coverage, in spite of free ...

Big Data Analytics + Virtual Clinical Semantic Network (vCSN): An Approach to Addressing the Increasing Clinical Nuances and Organ Involvement of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious vira...

Machine Learning for Biomedical Time Series Classification: From Shapelets to Deep Learning.

With the biomedical field generating large quantities of time series data, there has been a growing interest in developing and refining machine learni...

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images.

PURPOSE: Chest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assi...

Deep Learning of Computed Tomography Virtual Wedge Resection for Prediction of Histologic Usual Interstitial Pneumonitis.

The computed tomography (CT) pattern of definite or probable usual interstitial pneumonia (UIP) can be diagnostic of idiopathic pulmonary fibrosis an...

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.