Surveillance

Commentary: Towards machine learning-enabled epidemiology.

Ensemble modelling in descriptive epidemiology: burden of disease estimation.

Ensemble modelling is a quantitative method that combines information from multiple individual model...

Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19.

The COVID-19 pandemic is presenting a disproportionate impact on minorities in terms of infection ra...

End-stage renal disease at dialysis initiation: Epidemiology and mortality risks during the first year of hemodialysis.

Chronic kidney disease (CKD) treated by hemodialysis (HD) is a worldwide major public health problem...

Predictors of Survival among Male and Female Patients with Malignant Pleural Mesothelioma: A Random Survival Forest Analysis of Data from the 2000-2017 Surveillance, Epidemiology, and End Results Program.

BACKGROUND: Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy with a dismal p...

Clinical Databases for Breast Cancer Research.

Clinical database is a collection of clinical data related to patients, which can be used for analys...

Estimation of COVID-19 epidemic curves using genetic programming algorithm.

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm ...

Social Robot Interventions for People with Dementia: A Systematic Review on Effects and Quality of Reporting.

BACKGROUND: Using non-pharmacological interventions is a current approach in dementia care to manage...

Reporting of demographic data and representativeness in machine learning models using electronic health records.

OBJECTIVE: The development of machine learning (ML) algorithms to address a variety of issues faced ...

MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care.

The rise of digital data and computing power have contributed to significant advancements in artific...

Current Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting Evidence.

PURPOSE: Despite tremendous gains from deep learning and the promise of artificial intelligence (AI)...

Portable Automated Surveillance of Surgical Site Infections Using Natural Language Processing: Development and Validation.

OBJECTIVES: We present the development and validation of a portable NLP approach for automated surve...

How Does the Skeletal Oncology Research Group Algorithm's Prediction of 5-year Survival in Patients with Chondrosarcoma Perform on International Validation?

BACKGROUND: The Skeletal Oncology Research Group (SORG) machine learning algorithm for predicting su...

COVID-19 and the epistemology of epidemiological models at the dawn of AI.

The models used to estimate disease transmission, susceptibility and severity determine what epidemi...

Accelerating ophthalmic artificial intelligence research: the role of an open access data repository.

PURPOSE OF REVIEW: Artificial intelligence has already provided multiple clinically relevant applica...

Artificial intelligence or manufactured stupidity? The need for injury informaticians in the big data era.

The volume, velocity and variety of data collected about individuals have increased exponentially ov...

'I'm over the moon!': patient-perceived outcomes of hepatitis C treatment.

Understanding patient-perceived outcomes is crucial for assessing the effectiveness and acceptabilit...

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...

Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group.