Latest AI and machine learning research in surveillance for healthcare professionals.
Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstr...
BACKGROUND: Monitoring cardiac parameters is the fundamental aspect of every diagnostic process and is facilitated by electrocardiography (ECG) devices. This way, continuous state-of-the-art performance of ECG devices can be ensured. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategic...
BACKGROUND: Premature born infants or infants born sick require immediate medical attention and decreasing the stress imposed onto their body by the e...
Recent developments have enabled daily accumulated medical information to be converted into medical big data, and new evidence is expected to be creat...
In the diagnosis and treatment of throat disease, the application and development of combining voice analysis or endoscopic technology with artificial...
Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We sys...
PURPOSE: Symptoms are vital outcomes for cancer clinical trials, observational research, and population-level surveillance. Patient-reported outcomes ...
PURPOSE: Liver cancer is a global challenge, and disparities exist across multiple domains and throughout the disease continuum. However, liver cancer...
To perform a systematic review (SR) and meta-analysis (MA) of outcomes of robot-assisted laparoscopic pyeloplasty (RALP) for ureteropelvic junction (...
SARS-CoV-2 caused the first severe pandemic of the digital era. Computational approaches have been ubiquitously used in an attempt to timely and effec...
For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial Intelligence (AI) technologies being used in many diffe...
There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin ...
OBJECTIVE: We describe our approach to surveillance of reportable safety events captured in hospital data including free-text clinical notes. We hypot...
Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity ...
OBJECTIVE: Data sets with demographic imbalances can introduce bias in deep learning models and potentially amplify existing health disparities. We ev...
BACKGROUND: With the use of artificial intelligence and machine learning techniques for biomedical informatics, security and privacy concerns over the...
Logistic regression is a statistical tool of paramount significance in the field of epidemiology and ranks as one of the most frequently published mul...
BACKGROUND AND OBJECTIVES: Medical errors are a leading cause of death in the United States. Despite widespread adoption of patient safety reporting s...
There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for v...
BACKGROUND AND PURPOSE: Vulvovaginal candidiasis (VVC) is an opportunistic infection due to species, one of the most common genital tract diseases am...