Hospital-Based Medicine

Latest AI and machine learning research in hospital-based medicine for healthcare professionals.

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Using machine learning tools to predict outcomes for emergency department intensive care unit patients.

The number of critically ill patients has increased globally along with the rise in emergency visits...

Predicting the need for intubation in the first 24 h after critical care admission using machine learning approaches.

Early and accurate prediction of the need for intubation may provide more time for preparation and i...

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.

BACKGROUND: Explainability is one of the most heavily debated topics when it comes to the applicatio...

Risk factors and socio-economic burden in pancreatic ductal adenocarcinoma operation: a machine learning based analysis.

BACKGROUND: Surgical resection is the major way to cure pancreatic ductal adenocarcinoma (PDAC). How...

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.

Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic d...

A neural network for prediction of risk of nosocomial infection at intensive care units: a didactic preliminary model.

OBJECTIVE: To propose a preliminary artificial intelligence model, based on artificial neural networ...

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.

BACKGROUND: Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus ...

Perspectives of Child Life Specialists After Many Years of Working With a Humanoid Robot in a Pediatric Hospital: Narrative Design.

BACKGROUND: Child life specialists (CLSs) play an important role in supporting patients and their fa...

Single overnight stay after robot-assisted partial nephrectomy: a bi-center experience.

BACKGROUND: Despite hospital length of stay (LOS) being shorter for robot-assisted partial nephrecto...

Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission.

A 400-estimator gradient boosting classifier was trained to predict survival probabilities of trauma...

Machine Learning for Mortality Analysis in Patients with COVID-19.

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain)....

Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results ...

Machine-learning algorithms for predicting hospital re-admissions in sickle cell disease.

Reducing preventable hospital re-admissions in Sickle Cell Disease (SCD) could potentially improve o...

Accelerometer-Based Human Activity Recognition for Patient Monitoring Using a Deep Neural Network.

The objective of this study was to investigate the accuracy of a Deep Neural Network (DNN) in recogn...

Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates.

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whe...

The validity of Dutch health claims data for identifying patients with chronic kidney disease: a hospital-based study in the Netherlands.

BACKGROUND: Health claims data may be an efficient and easily accessible source to study chronic kid...

Safety and efficacy of robot-assisted versus open pancreaticoduodenectomy: a meta-analysis of multiple worldwide centers.

The objective of the study is to compare the safety and efficacy of robot-assisted pancreaticoduoden...

Detailed Analysis of Urinary Tract Infections After Robot-Assisted Radical Cystectomy.

To describe urinary tract infections (UTIs) after robot-assisted radical cystectomy (RARC) and inve...

Robotic versus open oncological gastric surgery in the elderly: a propensity score-matched analysis.

Although there is no agreement on a definition of elderly, commonly an age cutoff of ≥ 65 or 75 year...

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVI...

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