Public Health

Predicting of anaphylaxis in big data EMR by exploring machine learning approaches.

Anaphylaxis is a life-threatening allergic reaction that occurs suddenly after contact with an allergen. Epidemiological studies about anaphylaxis are very important in planning and evaluating new strategies that prevent this reaction, but also in providing a guide to the treatment of patients who have just suffered an anaphylactic reaction. Electronic Medical Records (EMR) are one of the most eff...

Estimation of the Basic Reproduction Number and Vaccination Coverage of Influenza in the United States (2017-18).

BACKGROUND: Determining the epidemic threshold parameter helps health providers calculate the coverage while guiding them in planning the process of vaccination strategy. Since the trend and mechanism of influenza is very similar in different countries, we planned a study with the mentioned goal by using data of US from 2017 to 2018.

Organization of the cancer network in SUS: evolution of the care model.

In the current context of epidemiological transition, demographic changes, changes in consumption and lifestyle habits, and pressure on care costs and...

CNN-Based Multimodal Human Recognition in Surveillance Environments.

In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images tak...

Kyasanur Forest Disease Classification Framework Using Novel Extremal Optimization Tuned Neural Network in Fog Computing Environment.

Kyasanur Forest Disease (KFD) is a life-threatening tick-borne viral infectious disease endemic to South Asia and has been taking so many lives every ...

The Elephant in the Room: Outbreak of Carfentanil Deaths in Minnesota and the Importance of Multiagency Collaboration.

Over a three-month period in early 2017, the Hennepin County Medical Examiner's Office investigated nine apparent opioid toxicity deaths that occurred...

Representing vaccine misinformation using ontologies.

BACKGROUND: In this paper, we discuss the design and development of a formal ontology to describe misinformation about vaccines. Vaccine misinformatio...

Artificial Intelligence in Public Health and Epidemiology.

OBJECTIVES:  To introduce and summarize current research in the field of Public Health and Epidemiology Informatics.

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcome...

Robotic selection for the rapid development of stable CHO cell lines for HIV vaccine production.

The production of envelope glycoproteins (Envs) for use as HIV vaccines is challenging. The yield of Envs expressed in stable Chinese Hamster Ovary (C...

PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions.

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathog...

Predicting Infectious Disease Using Deep Learning and Big Data.

Infectious disease occurs when a person is infected by a pathogen from another person or an animal. It is a problem that causes harm at both individua...

Molecular Genotyping of Species Isolated from Patients in Tehran, Iran, by Repetitive Element PCR Fingerprinting.

BACKGROUND & OBJECTIVE: is an opportunistic pathogen with high pathogenic and antibiotic-resistance potential and is also considered as one of the ma...

A machine learning method to monitor China's AIDS epidemics with data from Baidu trends.

BACKGROUND: AIDS is a worrying public health issue in China and lacks timely and effective surveillance. With the diffusion and adoption of the Intern...

Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models.

BACKGROUND: Timely understanding of public perceptions allows public health agencies to provide up-to-date responses to health crises such as infectio...

Mapping the transmission risk of Zika virus using machine learning models.

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expan...

Intelligent Network DisRuption Analysis (INDRA): A targeted strategy for efficient interruption of hepatitis C transmissions.

Hepatitis C virus (HCV) infection is a global public health problem. The implementation of public health interventions (PHI) to control HCV infection ...

A comparison of statistical and machine-learning techniques in evaluating the association between dietary patterns and 10-year cardiometabolic risk (2002-2012): the ATTICA study.

Statistical methods are usually applied in examining diet-disease associations, whereas factor analysis is commonly used for dietary pattern recogniti...

Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning.

As populations continue to age worldwide, the impact of sarcopenia on public health will continue to grow. The clinically relevant and increasingly co...

Screening for active pulmonary tuberculosis: Development and applicability of artificial neural network models.

Tuberculosis (TB) remains a significant public health challenge, motivated by the diversity of healthcare epidemiological settings, as other factors. ...