Latest AI and machine learning research in product alert for healthcare professionals.
Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data has been hindered by the massive and noisy nature of the data. Initial attempts to use social media data have relied on exact text matches to drugs of interest, and therefore suffer from the gap between formal drug lexicons and the informal nature of social media...
BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely and accurate prediction of post-stroke pneumonia would be highly valuable in clinical practice. Previous pneumonia risk score models were often built on simple statistical methods such as logistic regression. This study aims to investigate post-stro...
Drug-associated adverse events cause approximately 30 billion dollars a year of added health care expense, along with negative health outcomes includi...
Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation ...
This work presents a two-stage deep learning system for Named Entity Recognition (NER) and Relation Extraction (RE) from medical texts. These tasks ar...
We propose a new method for breast cancer screening from DCE-MRI based on a post-hoc approach that is trained using weakly annotated data (i.e., label...
We look at the recent application of deep learning (DL) methods in automated fine-grained segmentation of spectral domain optical coherence tomography...
Cochlear implants (CIs) are surgically implanted neural prosthetic devices that are used to treat severe-to-profound hearing loss. These devices are p...
Ricin is a highly toxic type 2 ribosome-inactivating protein (RIP) which is extracted from the seeds of castor beans. Ricin is considered a potential ...
PURPOSE: Adverse event (AE) identification in social media (SM) can be performed using various types of natural language processing (NLP) and machine ...
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and mu...
This study determined whether feeding the immunomodulating supplement, OmniGen-AF, to feedlot heifers would alter metabolic profiles to a glucose tole...
It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse...
Human-administered clinical scales are commonly used for quantifying motor performance and determining the course of therapy in post-stroke individual...
A 34-year-old woman with history of surgical correction (Takeuchi procedure) of anomalous left coronary artery from the pulmonary artery (ALCAPA) pres...
Extraction or segmentation of organ vessels is an important task for surgical planning and computer-aided diagnoses. This is a challenging task due to...
BACKGROUND: Hospital discharge summaries offer a potentially rich resource to enhance pharmacovigilance efforts to evaluate drug safety in real-world ...
Identifying medical persona from a social media post is critical for drug marketing, pharmacovigilance and patient recruitment. Medical persona classi...
Stroke is the main cause of disability after age 65, leaving survivors with sequels that require care and recovery treatment lasting years. It is esti...
BACKGROUND AND OBJECTIVE: Automatic extraction of adverse drug effect (ADE) mentions from biomedical texts is a challenging research problem that has ...