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Predicting outcomes following open abdominal aortic aneurysm repair using machine learning.

Patients undergoing open surgical repair of abdominal aortic aneurysm (AAA) have a high risk of post-operative complications. However, there are no widely used tools to predict surgical risk in this population. We used machine learning (ML) techniques to develop automated algorithms that predict 30-day outcomes following open AAA repair. The National Surgical Quality Improvement Program targeted v...

Post-stroke spontaneous motor recovery in mice can be predicted from acute-phase local field potential using machine learning.

Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors of motor recovery. Understanding the electrophysiological changes underlying spontaneous recovery could offer critical insight into recovery mechanisms and aid in predicting individual rehabilitation trajectories. In this study, we investigated the predictive power of local field potential...

Artificial Intelligence: Applications in Pharmacovigilance Signal Management.

Pharmacovigilance is the science of collection, detection, and assessment of adverse events associated with pharmaceutical products for the ongoing mo...

Construction of a deep learning model and identification of the pivotal characteristics of FGF7- and MGST1- positive fibroblasts in heart failure post-myocardial infarction.

Dysregulation of fibroblast function is closely associated with the occurrence of heart failure after myocardial infarction (post-MI HF). Myocardial f...

Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing...

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and e...

Changes in recreational drug use, reasons for those changes and their consequence during and after the COVID-19 pandemic in the UK.

Changes in drug use in the general population during the COVID-19 pandemic and their long-term consequences are not well understood. We employed natur...

Managing Screen Time in the Age of AI: Supporting Healthier Habits for Teens From the Inpatient Setting.

Psychiatric symptoms such as depressed mood, feelings of worthlessness, and anxiety can be exacerbated in adolescents by online content that mirrors t...

The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses.

This paper discusses the responsible use of artificial intelligence (AI) in public health and in medicine, and questions the development of AI ethics ...

Clinical implementation of AI-based screening for risk for opioid use disorder in hospitalized adults.

Adults with opioid use disorder (OUD) are at increased risk for opioid-related complications and repeated hospital admissions. Routine screening for p...

Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset, and recurrent nature. The development of an accu...

Circulating microRNA profiles are associated with acute pain and stress in castrated and tail docked lambs.

Maintaining animal welfare is an essential component of animal production systems. However, multiple measurements are required to inform an animal's w...

SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth and tumor formation. Understanding the intricate in...

Incorporating time as a third dimension in transcriptomic analysis using machine learning and explainable AI.

Transcriptomic data analysis entails the measurement of RNA transcript (gene expression products) abundance in a cell or a cell population at a single...

Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data.

Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress...

Predicting orthognathic surgery results as postoperative lateral cephalograms using graph neural networks and diffusion models.

Orthognathic surgery, or corrective jaw surgery, is performed to correct severe dentofacial deformities and is increasingly sought for cosmetic purpos...

Risk of bias assessment of post-stroke mortality machine learning predictive models: Systematic review.

BACKGROUND: Stroke is a major cause of mortality and permanent disability worldwide. Precise prediction of post-stroke mortality is essential for guid...

Querying the capability of the post-HoLEP endoscopic aspect of the membranous urethral mucosa in predicting urinary incontinence: a prospective AI-based analysis.

INTRODUCTION: Transient stress urinary incontinence (SUI) after holmium laser enucleation of prostate (HoLEP) is commonly linked to intraoperative inj...

How Can Robotic Devices Help Clinicians Determine the Treatment Dose for Post-Stroke Arm Paresis?

Upper limb training dose after stroke is usually quantified by time and repetitions. This study analyzed upper limb motor training dose in stroke part...

Joint fusion of EHR and ECG data using attention-based CNN and ViT for predicting adverse clinical endpoints in percutaneous coronary intervention patients.

Predicting post-Percutaneous Coronary Intervention (PCI) outcomes is crucial for effective patient management and quality improvement in healthcare. H...