Staffing & Scheduling

Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU.

The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, ECG recording is often subject to various noises, which can limit its clinical evaluation. To address this issue, we propose a novel Transformer-based convolutional neural network framework with adaptively parametric ReLU (APtrans-CNN) for ECG signal denoising. The proposed APtrans-CNN architecture c...

Environmental Sustainability and AI in Radiology: A Double-Edged Sword.

According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large...

DrugBank 6.0: the DrugBank Knowledgebase for 2024.

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and relate...

An infection prediction model developed from inpatient data can predict out-of-hospital COVID-19 infections from wearable data when controlled for dataset shift

The COVID-19 pandemic highlighted the importance of early detection of illness and the need for health monitoring solutions outside of the hospital se...

Multimodal representation learning for medical analytics - a systematic literature review.

Machine learning-based analytics over uni-modal medical data has shown considerable promise and is now routinely deployed in diagnostic procedures. H...

Deep Learning-Assisted Analysis of Immunopeptidomics Data.

Liquid chromatography-coupled mass spectrometry (LC-MS/MS) is the primary method to obtain direct evidence for the presentation of disease- or patient...

Identifying best fall-related balance factors and robotic-assisted gait training attributes in 105 post-stroke patients using clinical machine learning models.

BACKGROUND: Despite the promising effects of robot-assisted gait training (RAGT) on balance and gait in post-stroke rehabilitation, the optimal predic...

A fuzzy TOPSIS-based approach for prioritizing low-impact development methods in high-density residential areas.

The study successfully implemented six low-impact development (LID) methods to manage surface runoff in urban areas: green roof, infiltration trench, ...

Machine learning models to predict surgical case duration compared to current industry standards: scoping review.

BACKGROUND: Surgical waiting lists have risen dramatically across the UK as a result of the COVID-19 pandemic. The effective use of operating theatres...

Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers.

Current and future healthcare professionals are generally not trained to cope with the proliferation of artificial intelligence (AI) technology in hea...

Beyond Distribution Shift: Spurious Features Through the Lens of Training Dynamics.

Deep Neural Networks (DNNs) are prone to learning spurious features that correlate with the label during training but are irrelevant to the learning p...

Initial Experience of Robot-Assisted Simple Prostatectomy with Hugo Robot-Assisted Surgery System: Step-by-Step Description of Two Different Techniques.

There are only a few clinical data on nononcologic procedures performed with the new Hugo™ robot-assisted surgery (RAS) system. Robot-assisted simple...

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials.

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is imp...

Automatic assessment of patient eligibility by utilizing NLP and rule-based analysis.

Assessment of patient eligibility is an essential process in the clinical trial but there are a lot of manual processes involved. Natural Language Pro...

Adaptive Learning based Upper-Limb Rehabilitation Training System with Collaborative Robot.

Rehabilitation training for patients with motor disabilities usually requires specialized devices in rehabilitation centers. Home-based multi-purpose ...

Artificial intelligence and cytopathology: Where are we heading?: In this first of a two-part series, a recent burst of research in artificial intelligence is showing how the algorithms can expand beyond gynecologic applications-and how they might shift responsibilities for the cytology workforce: In this first of a two-part series, a recent burst of research in artificial intelligence is showing how the algorithms can expand beyond gynecologic applications-and how they might shift responsibilities for the cytology workforce.

Translating AI to Clinical Practice: Overcoming Data Shift with Explainability.

To translate artificial intelligence (AI) algorithms into clinical practice requires generalizability of models to real-world data. One of the main ob...

Progress made in the efficacy and viability of deep-learning-based noise reduction.

Recent years have brought considerable advances to our ability to increase intelligibility through deep-learning-based noise reduction, especially for...

Artificial intelligence chatbots will revolutionize how cancer patients access information: ChatGPT represents a paradigm-shift.

On November 30, 2022, OpenAI enabled public access to ChatGPT, a next-generation artificial intelligence with a highly sophisticated ability to write,...

What every dentist needs to know about the use of artificial intelligence in dentistry.

Artificial intelligence (AI) is a division of computer science that allows machines to emulate human cognitive processes. In dentistry, AI is applied ...