Hospital-Based Medicine

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 networks, for predicting the risk of nosocomial infection at intensive care units.

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 perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement re...

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 2 (SARS-CoV-2) could aid patient management and al...

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 nephrectomy (RAPN) compared to its open counterpart, severa...

Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of art...

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 patients. The National Trauma Data Bank (NTDB) pr...

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). Survival analysis, logistic regression, and machi...

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 across settings and populations. With recent uptak...

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 outcomes and decrease healthcare costs. In a retros...

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 pancreaticoduodenectomy (PD) with open PD. The PubMed, EMBASE and C...

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

To describe urinary tract infections (UTIs) after robot-assisted radical cystectomy (RARC) and investigate the variables associated with it. A retro...

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 years is used. Even if robot-assisted surgery is a val...

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 (COVID19) pneumonia. We aimed to construct a system bas...

[A brief history of artificial intelligence].

For more than a decade, we have witnessed an acceleration in the development and the adoption of artificial intelligence (AI) technologies. In medicin...

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of in Relation to Hydrographic Conditions.

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea...

TAPER: Time-Aware Patient EHR Representation.

Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data i...

An Efficient Deep Learning Based Method for Speech Assessment of Mandarin-Speaking Aphasic Patients.

Speech assessment is an important part of the rehabilitation process for patients with aphasia (PWA). Mandarin speech lucidity features such as articu...

Machine learning models to predict length of stay and discharge destination in complex head and neck surgery.

BACKGROUND: This study develops machine learning (ML) algorithms that use preoperative-only features to predict discharge-to-nonhome-facility (DNHF) a...

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction.

Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of t...

Robot-Assisted Radical Cystectomy for Nonmetastatic Urothelial Carcinoma of Urinary Bladder: A Comparison Between Intracorporeal Versus Extracorporeal Orthotopic Ileal Neobladder.

To compare surgical, oncologic, functional outcomes and complication rate between intracorporeal neobladder (ICNB) and extracorporeal neobladder (ECN...