Latest AI and machine learning research in identifying and reporting dependent adult abuse for healthcare professionals.
BACKGROUND: Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying...
The exponential increase in the volume and complexity of healthcare data presents new challenges to researchers and clinicians in analysis and interpretation. The requirement for new strategies to extract meaningful information from large, noisy datasets has led to the development of the field of big data analytics. Artificial intelligence (AI) is a general-purpose technology in which machines car...
The effectiveness of machine learning models to provide accurate and consistent results in drug discovery and clinical decision support is strongly de...
Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. ...
Although artificial intelligence models have demonstrated high accuracy in identifying specific orthopedic implant models from imaging, which is an im...
The association between physical appearance and income has been of central interest in social science. However, most previous studies often measured p...
Social robots are increasingly penetrating our daily lives. They are used in various domains, such as healthcare, education, business, industry, and c...
The purpose of medical image registration is to find geometric transformations that align two medical images so that the corresponding voxels on two i...
A novel Machine Learning (ML) method based on Neural Networks (NN) is proposed to assess radio-frequency (RF) exposure generated by WiFi sources in in...
INTRODUCTION: The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement and the Prediction...
Human motion prediction, which aims to predict future human poses given past poses, has recently seen increased interest. Many recent approaches are b...
The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specif...
There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has...
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 ...
In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, huma...
An objective measure of pain remains an unmet need of people with chronic pain, estimated to be 1/3 of the adult population in the United States. The ...
Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in o...
cardiovascular complications (CVC) are the leading cause of death in patients with chronic kidney disease (CKD). Standard cardiovascular disease risk...
Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when repor...