Hospital-Based Medicine

Biocide susceptibilities and biofilm-forming capacities of Acinetobacter baumannii clinical isolates from Malaysia.

INTRODUCTION: Acinetobacter baumannii is a Gram-negative nosocomial pathogen that has the capacity to develop resistance to all classes of antimicrobial compounds. However, very little is known regarding its susceptibility to biocides (antiseptics and disinfectants) and capacity to form biofilms, particularly for Malaysian isolates.

Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach.

BACKGROUND: Severe traumatic brain injury (TBI) is a leading cause of mortality in children, but the accurate prediction of outcomes at the point of admission remains very challenging. Admission laboratory results are a promising potential source of prognostic data, but have not been widely explored in paediatric cohorts. Herein, we use machine-learning methods to analyse 14 different serum parame...

Machine learning methods applied to audit of surgical outcomes after treatment for cancer of the head and neck.

Most surgical specialties have attempted to address concerns about unfair comparison of outcomes by "risk-adjusting" data to benchmark specialty-speci...

Artificial intelligence in the interpretation of breast cancer on MRI.

Advances in both imaging and computers have led to the rise in the potential use of artificial intelligence (AI) in various tasks in breast imaging, g...

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health ...

Readmission prediction using deep learning on electronic health records.

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. I...

On-field player workload exposure and knee injury risk monitoring via deep learning.

In sports analytics, an understanding of accurate on-field 3D knee joint moments (KJM) could provide an early warning system for athlete workload expo...

Predicting mechanical restraint of psychiatric inpatients by applying machine learning on electronic health data.

OBJECTIVE: Mechanical restraint (MR) is used to prevent patients from harming themselves or others during inpatient treatment. The objective of this s...

An integrated model using the Taguchi method and artificial neural network to improve artificial kidney solidification parameters.

BACKGROUND: Hemodialysis mainly relies on the "artificial kidney," which plays a very important role in temporarily or permanently substituting for th...

A Real-Time Early Warning System for Monitoring Inpatient Mortality Risk: Prospective Study Using Electronic Medical Record Data.

BACKGROUND: The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfe...

Machine Learning Approach to Inpatient Violence Risk Assessment Using Routinely Collected Clinical Notes in Electronic Health Records.

IMPORTANCE: Inpatient violence remains a significant problem despite existing risk assessment methods. The lack of robustness and the high degree of e...

Development of machine learning algorithms for prediction of mortality in spinal epidural abscess.

BACKGROUND CONTEXT: In-hospital and short-term mortality in patients with spinal epidural abscess (SEA) remains unacceptably high despite diagnostic a...

Comparison of machine learning models for seizure prediction in hospitalized patients.

OBJECTIVE: To compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify l...

Neural networks versus Logistic regression for 30 days all-cause readmission prediction.

Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a reco...

Efficacy of Cardiac Rehabilitation with Assistance from Hybrid Assistive Limb in Patients with Chronic Heart Failure: Protocol for a Randomized Controlled Study.

The hybrid assistive limb (HAL) provides motion assistance based on bioelectrical signals detected on the skin surface when muscle forces are generate...

A Radical Proposition: Opioid-sparing Prostatectomy.

Radical prostatectomy has largely become a procedure requiring a single day in the hospital with improving convalescence. Pre-operative counseling on ...

Deep Learning Preoperatively Predicts Value Metrics for Primary Total Knee Arthroplasty: Development and Validation of an Artificial Neural Network Model.

BACKGROUND: The objective is to develop and validate an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charg...

FriWalk robotic walker: usability, acceptance and UX evaluation after a pilot study in a real environment.

: Scientific evidence supports that prevention strategies like multicomponent physical exercise help avoiding functional decline, falls and frailty. T...

Deep-Learning Language-Modeling Approach for Automated, Personalized, and Iterative Radiology-Pathology Correlation.

PURPOSE: Radiology-pathology correlation has long been foundational to continuing education, peer learning, quality assurance, and multidisciplinary p...

Predicting Inpatient Payments Prior to Lower Extremity Arthroplasty Using Deep Learning: Which Model Architecture Is Best?

BACKGROUND: Recent advances in machine learning have given rise to deep learning, which uses hierarchical layers to build models, offering the ability...