Surveillance

Artificial Intelligence in Nutrients Science Research: A Review.

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnost...

Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures.

BACKGROUND & OBJECTIVE: Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for predicting risks and decision-making processes. The purpose of this paper is to provide an effective mathematical model for predicting the spread of Cov...

Deep learning-based computer-aided diagnosis in screening breast ultrasound to reduce false-positive diagnoses.

A major limitation of screening breast ultrasound (US) is a substantial number of false-positive biopsy. This study aimed to develop a deep learning-b...

Delimiting cryptic morphological variation among human malaria vector species using convolutional neural networks.

Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit specie...

Development of a Self-Harm Monitoring System for Victoria.

The prevention of suicide and suicide-related behaviour are key policy priorities in Australia and internationally. The World Health Organization has ...

Applying Convolutional Neural Networks to Predict the ICD-9 Codes of Medical Records.

The International Statistical Classification of Disease and Related Health Problems (ICD) is an international standard system for categorizing and rep...

Accurate spatiotemporal mapping of drug overdose deaths by machine learning of drug-related web-searches.

Persons who inject drugs (PWID) are at increased risk for overdose death (ODD), infections with HIV, hepatitis B (HBV) and hepatitis C virus (HCV), an...

Surveillance of SARS-CoV-2 in sewage and wastewater treatment plants in Mexico.

The SARS-CoV-2 virus causing COVID-19 is spread in sewage by the stool of infected individuals, and viral material in sewage can be quantified using m...

EventEpi-A natural language processing framework for event-based surveillance.

According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, i...

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine...

Evaluation of Five International HBV Treatment Guidelines: Recommendation for Resource-Limited Developing Countries Based on the National Study in Nepal.

Hepatitis B virus (HBV) infects the liver, causing cirrhosis and cancer. In developed countries, five international guidelines have been used to make ...

COVID-19 Surveillance in a Primary Care Sentinel Network: In-Pandemic Development of an Application Ontology.

BACKGROUND: Creating an ontology for COVID-19 surveillance should help ensure transparency and consistency. Ontologies formalize conceptualizations at...

Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.

BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent ...

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

INTRODUCTION: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domain...

Comparing machine learning with case-control models to identify confirmed dengue cases.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to dete...

Guidelines for clinical trials using artificial intelligence - SPIRIT-AI and CONSORT-AI.

The rapidly growing use of artificial intelligence in pathology presents a challenge in terms of study reporting and methodology. The existing guideli...

Predicting population health with machine learning: a scoping review.

OBJECTIVE: To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe whic...

Magnetic resonance imaging-ultrasound fusion-targeted biopsy combined with systematic 12-core ultrasound-guided biopsy improves the detection of clinically significant prostate cancer: Are we ready to abandon the systematic approach?

BACKGROUND: Multiparametric (mp) magnetic resonance imaging (MRI)-ultrasound fusion-targeted biopsy (TB) has improved the detection of clinically sign...

Recommendations for Reporting Machine Learning Analyses in Clinical Research.

Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents imp...

COVID-SAFE: An IoT-Based System for Automated Health Monitoring and Surveillance in Post-Pandemic Life.

In the early months of the COVID-19 pandemic with no designated cure or vaccine, the only way to break the infection chain is self-isolation and maint...