Public Health & Policy

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos.

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overco...

DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network.

At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCa...

Characterizing chronological accumulation of comorbidities in healthy veterans: a computational approach.

Understanding patient accumulation of comorbidities can facilitate healthcare strategy and personalized preventative care. We applied a directed netwo...

A remote management system for control and surveillance of echinococcosis: design and implementation based on internet of things.

BACKGROUND: As a neglected cross-species parasitic disease transmitted between canines and livestock, echinococcosis remains a global public health co...

Machine Learning Algorithms for Predicting Fatty Liver Disease.

BACKGROUND: Fatty liver disease (FLD) has become a rampant condition. It is associated with a high rate of morbidity and mortality in a population. Th...

Autonomous Marine Robot Based on AI Recognition for Permanent Surveillance in Marine Protected Areas.

The world's oceans are one of the most valuable sources of biodiversity and resources on the planet, although there are areas where the marine ecosyst...

Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach.

BACKGROUND: Controlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of soci...

Artificial Intelligence and Liability in Medicine: Balancing Safety and Innovation.

Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy co...

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.

OBJECTIVES: During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both ...

Predicting postoperative opioid use with machine learning and insurance claims in opioid-naïve patients.

BACKGROUND: The clinical impact of postoperative opioid use requires accurate prediction strategies to identify at-risk patients. We utilize preoperat...

A deep cascaded segmentation of obstructive sleep apnea-relevant organs from sagittal spine MRI.

PURPOSE: The main purpose of this work was to develop an efficient approach for segmentation of structures that are relevant for diagnosis and treatme...

Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.

Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of ...

Artificial neural networks versus LASSO regression for the prediction of long-term survival after surgery for invasive IPMN of the pancreas.

Prediction of long-term survival in patients with invasive intraductal papillary mucinous neoplasm (IPMN) of the pancreas may aid in patient assessmen...

A region-specific clustering approach to investigate risk-factors in mortality rate during COVID-19: comprehensive statistical analysis from 208 countries.

Since the outbreak of the novel coronavirus, COVID-19 has continuously spread across the globe briskly. However, since its existence, the symptoms of ...

Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data.

The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and...

Integrating human services and criminal justice data with claims data to predict risk of opioid overdose among Medicaid beneficiaries: A machine-learning approach.

Health system data incompletely capture the social risk factors for drug overdose. This study aimed to improve the accuracy of a machine-learning algo...

Machine Learning Prediction and Experimental Validation of Antigenic Drift in H3 Influenza A Viruses in Swine.

The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat...

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation.

BACKGROUND: Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, li...

Improving the precision of modeling the incidence of hemorrhagic fever with renal syndrome in mainland China with an ensemble machine learning approach.

OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar di...

Multi-source Seq2seq guided by knowledge for Chinese healthcare consultation.

Online healthcare consultation offers people a convenient way to consult doctors. In this paper, we aim at building a generative dialog system for Chi...