Hospital-Based Medicine

Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test.

BACKGROUND: The early clinical course of COVID-19 can be difficult to distinguish from other illness...

The role of artificial intelligence in surgical patient perioperative management.

Perioperative medicine is a patient-centered, multidisciplinary and integrated clinical practice tha...

Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care.

A plea to merge clinical and public health practices: reasons and consequences.

BACKGROUND: Revisiting professionalism, both as a medical ideal and educational topic, this paper as...

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...

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...

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...

Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges.

Machine learning offers great opportunities to streamline and improve clinical care from the perspec...

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 ...

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...

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...

[A brief history of artificial intelligence].

For more than a decade, we have witnessed an acceleration in the development and the adoption of art...

TAPER: Time-Aware Patient EHR Representation.

Effective representation learning of electronic health records is a challenging task and is becoming...