Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Permutation entropy is computationally efficient, robust to outliers, and effective to measure complexity of time series. We used this technique to quantify the complexity of continuous vital signs recorded from patients with traumatic brain injury (TBI). Using permutation entropy calculated from early vital signs (initial 10-20% of patient hospital stay time), we built classifiers to predict in-h...
BACKGROUND: In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that nearly $30.8 billion in hospital care cost during 2006 was potentially preventable, with heart diseases being responsible for about 31% of that amount.
STUDY OBJECTIVE: To determine the factors that allow for a safe outpatient robotic-assisted minimally invasive gynecologic oncology surgery procedure.
Laparoscopic cholecystectomy is widely considered as the treatment of choice for acute cholecystitis. The safety of the procedure and its minimal inva...
We present a novel design of an intelligent robotic hospital bed, named Flexbed, with autonomous navigation ability. The robotic bed is developed for ...
A commonly used method for evaluating a hospital's performance on an outcome is to compare the hospital's observed outcome rate to the hospital's expe...
PURPOSE: This study investigates the effectiveness of Lokomat + conventional therapy in recovering walking ability in non-ambulatory subacute stroke s...
BACKGROUND: A precondition for the success of the prevention of SSI is the complete realisation of the proven anti-infective measures in form of the m...
Longitudinal prediction from electronic health records (EHRs) is limited by the sparsity and irregularity in patient trajectories, and knowledge augme...
Automatic coding from clinical notes has been studied extensively for International Classification of Diseases (ICD) codes, yet broad Current Procedur...
Routine childhood growth surveillance is a cornerstone of pediatric care. Growth pattern abnormalities are often early manifestations of chronic disea...
Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (EHR) modeli...
Agitation fluctuates over short time horizons in people living with dementia, yet continuous physiological information for anticipating next-day risk ...
Unobtrusive ballistocardiographic (BCG) sensing is a promising modality for long-term sleep-apnea monitoring, yet it remains unclear which signal feat...
Currently used sepsis severity indices rely on fixed variables and weights established decades ago, which are coarsely discretized and calibrated to a...
Multimodal product models can complete missing e-commerce attributes, yet current methods still optimize attribute-answer accuracy without verifying v...
Large Language Models (LLMs) are increasingly applied to clinical prediction tasks such as in-hospital mortality and readmission from electronic healt...
Clinical records contain rich evidence about patient state, but converting that evidence into reliable, structured knowledge graphs remains difficult ...
Ensuring that Extended Reality (XR) environments are age-appropriate is an important regulatory and safety challenge. However, current age assurance o...
Background: Machine learning models leveraging electronic health records (EHRs) can support earlier detection of sepsis in intensive care units (ICUs)...