Surveillance

Systematic analysis of supervised machine learning as an effective approach to predicate β-lactam resistance phenotype in Streptococcus pneumoniae.

Streptococcus pneumoniae is the most common human respiratory pathogen, and β-lactam antibiotics hav...

A Systematic Search over Deep Convolutional Neural Network Architectures for Screening Chest Radiographs.

Chest radiographs are primarily employed for the screening of pulmonary and cardio-/thoracic conditi...

Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence.

Increasingly, interventions aimed at improving care are likely to use such technologies as machine l...

When two heads are better than one: nutritional epidemiology meets machine learning.

Artificial intelligence and the future of global health.

Concurrent advances in information technology infrastructure and mobile computing power in many low ...

AI Surveillance during Pandemics: Ethical Implementation Imperatives.

Artificial intelligence surveillance can be used to diagnose individual cases, track the spread of C...

Big Data in Ophthalmology.

Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as ...

Future Vision 2020 and Beyond-5 Critical Trends in Eye Research.

Ophthalmology has been at the forefront of many innovations in basic science and clinical research. ...

Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.

INTRODUCTION: Identification of adverse events and determination of their seriousness ensures timely...

Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study.

PURPOSE: To develop a deep convolutional neural network (CNN) model to categorize multiphase CT and ...

FOBI: an ontology to represent food intake data and associate it with metabolomic data.

Nutrition research can be conducted by using two complementary approaches: (i) traditional self-repo...

Natural Language Processing for the Identification of Surgical Site Infections in Orthopaedics.

BACKGROUND: The identification of surgical site infections for infection surveillance in hospitals d...

Comparison of prostate cancer detection rates between magnetic resonance imaging-targeted biopsy and transrectal ultrasound-guided biopsy according to Prostate Imaging Reporting and Data System in patients with PSA ≥4 ng/mL: a systematic review and meta-analysis.

BACKGROUND: Previous studies have investigated magnetic resonance imaging-targeted biopsy (MRI-TBx) ...

Applying Natural Language Processing to Evaluate News Media Coverage of Bullying and Cyberbullying.

Bullying events have frequently been the focus of coverage by news media, including news stories abo...

Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate 'Real World' Evidence of Comparative Effectiveness and Safety.

Research that makes secondary use of administrative and clinical healthcare databases is increasingl...

[An overview of validation methods based on the medical claims database].

Medical claims database is an important source of data for studying the characteristics, and burden ...

Enabling Data-Driven Clinical Quality Assurance: Predicting Adverse Event Reporting in Clinical Trials Using Machine Learning.

INTRODUCTION: Adverse event (AE) under-reporting has been a recurrent issue raised during health aut...

IDOMEN: An Extension of Infectious Disease Ontology for MENingitis.

In sub-Saharan African countries the prevention and control of epidemic diseases requires the improv...

A New Approach to Compare the Performance of Two Classification Methods of Causes of Death for Timely Surveillance in France.

Timely mortality surveillance in France is based on the monitoring of electronic death certificates ...

Signals Among Signals: Prioritizing Nongenetic Associations in Massive Data Sets.

Massive data sets are often regarded as a panacea to the underpowered studies of the past. At the sa...

A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize...