Public Health & Policy

Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations.

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intell...

A CNN-transformer fusion network for COVID-19 CXR image classification.

The global health crisis due to the fast spread of coronavirus disease (Covid-19) has caused great danger to all aspects of healthcare, economy, and other aspects. The highly infectious and insidious nature of the new coronavirus greatly increases the difficulty of outbreak prevention and control. The early and rapid detection of Covid-19 is an effective way to reduce the spread of Covid-19. Howev...

Public sentiment on the global outbreak of monkeypox: an unsupervised machine learning analysis of 352,182 twitter posts.

OBJECTIVES: This study aimed to study the public's sentiments on the current monkeypox outbreaks via an unsupervised machine learning analysis of soci...

Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets.

This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles usin...

Development and Evaluation of Machine Learning-Based High-Cost Prediction Model Using Health Check-Up Data by the National Health Insurance Service of Korea.

In this study, socioeconomic, medical treatment, and health check-up data from 2010 to 2017 of the National Health Insurance Service (NHIS) of Korea w...

Forecasting Weekly Dengue Cases by Integrating Google Earth Engine-Based Risk Predictor Generation and Google Colab-Based Deep Learning Modeling in Fortaleza and the Federal District, Brazil.

Efficient and accurate dengue risk prediction is an important basis for dengue prevention and control, which faces challenges, such as downloading and...

Nondestructive and multiplex differentiation of pathogenic microorganisms from spoilage microflora on seafood using paper chromogenic array and neural network.

Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a pap...

Optimal Tasking of Ground-Based Sensors for Space Situational Awareness Using Deep Reinforcement Learning.

Space situational awareness (SSA) is becoming increasingly challenging with the proliferation of resident space objects (RSOs), ranging from CubeSats ...

A deep learning approach to real-time HIV outbreak detection using genetic data.

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential...

Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications.

Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to uni...

Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools.

Drowning is a major health issue worldwide. The World Health Organization's global report on drowning states that the highest rates of drowning deaths...

Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images.

Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbre...

A Two-Mode Underwater Smart Sensor Object for Precision Aquaculture Based on AIoT Technology.

Monitoring the status of culture fish is an essential task for precision aquaculture using a smart underwater imaging device as a non-intrusive way of...

Performance evaluation of machine learning and Computer Coded Verbal Autopsy (CCVA) algorithms for cause of death determination: A comparative analysis of data from rural South Africa.

Computer Coded Verbal Autopsy (CCVA) algorithms are commonly used to determine the cause of death (CoD) from questionnaire responses extracted from ve...

Deep learning techniques for detecting and recognizing face masks: A survey.

The year 2020 brought many changes to the lives of people all over the world with the outbreak of COVID-19; we saw lockdowns for months and deaths of ...

From real-world electronic health record data to real-world results using artificial intelligence.

With the worldwide digitalisation of medical records, electronic health records (EHRs) have become an increasingly important source of real-world data...

Recognition and segmentation of individual pigs based on Swin Transformer.

Recognition of individual pigs is critical to the monitoring of pig body size and physiological health status in large-scale pig farms. In this study,...

A Multimodal Data Processing System for LiDAR-Based Human Activity Recognition.

Increasingly, the task of detecting and recognizing the actions of a human has been delegated to some form of neural network processing camera or wear...

A Precision Health Service for Chronic Diseases: Development and Cohort Study Using Wearable Device, Machine Learning, and Deep Learning.

This paper presents an integrated and scalable precision health service for health promotion and chronic disease prevention. Continuous real-time moni...

Verbal Autopsy as a Tool for Defining Causes of Death in Specific Healthcare Contexts: Study of Applicability through a Traditional Literature Review.

Autopsy examination, the gold standard for defining causes of death, is often difficult to apply in certain health care settings, especially in develo...