Hospital-Based Medicine

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...

RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection.

Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain fu...

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)...

Effect of Machine Learning on Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical Services: A Randomized Clinical Trial.

IMPORTANCE: Emergency medical dispatchers fail to identify approximately 25% of cases of out-of-hosp...

Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.

Artificial intelligence can facilitate clinical decision making by considering massive amounts of me...

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...

Response to: Williams, Kotamarti, and Schulman re: "Outpatient Robot-Assisted Radical Prostatectomy: Are Patients Ready for Same-Day Discharge?" by Dobbs et al.

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...

Prediction of in-hospital mortality in patients on mechanical ventilation post traumatic brain injury: machine learning approach.

BACKGROUND: The study aimed to introduce a machine learning model that predicts in-hospital mortalit...

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.

Vital signs assessed in initial clinical encounters predict COVID-19 mortality in an NYC hospital system.

Timely and effective clinical decision-making for COVID-19 requires rapid identification of risk fac...

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...

Machine learning approach for predicting Fusarium culmorum and F. proliferatum growth and mycotoxin production in treatments with ethylene-vinyl alcohol copolymer films containing pure components of essential oils.

Fusarium culmorum and F. proliferatum can grow and produce, respectively, zearalenone (ZEA) and fumo...