Latest AI and machine learning research in surveillance for healthcare professionals.
BACKGROUND: Vaccine safety surveillance is important because it is related to vaccine hesitancy, which affects vaccination rate. To increase confidence in vaccination, the active monitoring of vaccine adverse events is important. For effective active surveillance, we developed and verified a machine learning-based active surveillance system using national claim data.
Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrovirus known as Human T-cell Lymphotropic virus (HTLV). Out of the four different types of HTLVs, HTLV-1 is most prominent in scourging over 20 million ...
Although artificial intelligence models have demonstrated high accuracy in identifying specific orthopedic implant models from imaging, which is an im...
In this study, we aimed to propose a novel diabetes index for the risk classification based on machine learning techniques with a high accuracy for di...
The association between physical appearance and income has been of central interest in social science. However, most previous studies often measured p...
BACKGROUND: Self-reported symptoms during the COVID-19 pandemic have been used to train artificial intelligence models to identify possible infection ...
BACKGROUND AND OBJECTIVE: To achieve the full potential of deep learning (DL) models, such as understanding the interplay between model (size), traini...
INTRODUCTION: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction...
Due to the prevalence of globalization and the surge in people's traffic, diseases are spreading more rapidly than ever and the risks of sporadic cont...
There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has...
Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access...
OBJECTIVE: Evaluate the completeness of reporting of prognostic prediction models developed using machine learning methods in the field of oncology.
INTRODUCTION: Standards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in ...
Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that lev...
Artificial Intelligence can be leveraged to analyze great amounts of data. It can be used on images or textual data to define the epidemiology of dise...
Emotion is a form of high-level paralinguistic information that is intrinsically conveyed by human speech. Automatic speech emotion recognition is an ...
In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, huma...
Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in o...
Surveillance cameras are being installed in many primary daily living places to maintain public safety. In this video-surveillance context, anomalies ...
cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk...