Hospital-Based Medicine

An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT.

Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characteriz...

Robotic Versus Laparoscopic Adrenalectomy: Pluriannual Experience in a High-Volume Center Evaluating Indications and Results.

Robotic adrenalectomy offers several clinical benefits if compared with laparoscopic adrenalectomy;...

A survey of deep learning models in medical therapeutic areas.

Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learn...

Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach.

Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient...

Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission.

The aim of this study was to develop a predictive model of pediatric mortality in the early stages o...

Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes.

Machine learning has been suggested as a means of identifying individuals at greatest risk for hospi...

Recurrent disease progression networks for modelling risk trajectory of heart failure.

MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series rec...

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

IMPORTANCE: In the US, more than 600 000 adults will experience an acute myocardial infarction (AMI)...

Comparison of Robot-Assisted and Laparoscopic Partial Nephrectomy for Renal Hilar Tumors: Results from a Tertiary Referral Center.

To compare perioperative, functional, and oncologic outcomes between robot-assisted partial nephrec...

Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team.

The use of artificial intelligence (AI) algorithms in the field of radiology is becoming more common...

Image registration: Maximum likelihood, minimum entropy and deep learning.

In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise an...

Evidence of Gender Differences in the Diagnosis and Management of Coronavirus Disease 2019 Patients: An Analysis of Electronic Health Records Using Natural Language Processing and Machine Learning.

The impact of sex and gender in the incidence and severity of coronavirus disease 2019 (COVID-19) r...

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 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 ...