Hospital-Based Medicine

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are limited studies on the application of the decision tree algorithm with various functional and environmental variables to identify stroke patients with a high possibility of home discharge. The present study aimed to identify the factors, including f...

Autonomous Robot for Removing Superficial Traumatic Blood.

: To remove blood from an incision and find the incision spot is a key task during surgery, or else over discharge of blood will endanger a patient's life. However, the repetitive manual blood removal involves plenty of workload contributing fatigue of surgeons. Thus, it is valuable to design a robotic system which can automatically remove blood on the incision surface. : In this paper, we design ...

Machine Learning of Patient Characteristics to Predict Admission Outcomes in the Undiagnosed Diseases Network.

IMPORTANCE: The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refract...

Machine learning model for predicting severity prognosis in patients infected with COVID-19: Study protocol from COVID-AI Brasil.

The new coronavirus, which began to be called SARS-CoV-2, is a single-stranded RNA beta coronavirus, initially identified in Wuhan (Hubei province, Ch...

An Artificial Neural Networks Model for Early Predicting In-Hospital Mortality in Acute Pancreatitis in MIMIC-III.

BACKGROUND: Early and accurate evaluation of severity and prognosis in acute pancreatitis (AP), especially at the time of admission is very significan...

Selection of Clinical Text Features for Classifying Suicide Attempts.

Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electro...

AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.

Allergy mention normalization is challenging because of the wide range of possible allergens including medications, foods, plants, animals, and consum...

Human-dog relationships as a working framework for exploring human-robot attachment: a multidisciplinary review.

Robotic agents will be life-long companions of humans in the foreseeable future. To achieve such successful relationships, people will likely attribut...

Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities.

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM ca...

Multi-Institutional Assessment of Routine Same Day Discharge Surgery for Robot-Assisted Radical Prostatectomy. Letter.

Machine learning combining CT findings and clinical parameters improves prediction of length of stay and ICU admission in torso trauma.

OBJECTIVE: To develop machine learning (ML) models capable of predicting ICU admission and extended length of stay (LOS) after torso (chest, abdomen, ...

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 characterizing pelvic fracture severity. Tile AO/OTA grade ba...

Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.

PURPOSE: In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple bi...

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; however, its superiority is still under debate. T...

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 learning is presently the one with the most impact. Mor...

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 outcomes. Natural language processing offers new ...

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 of intensive care unit (ICU) admission using machin...

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 hospital readmission, including psychiatric readmission...

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

MOTIVATION: Recurrent neural networks (RNN) are powerful frameworks to model medical time series records. Recent studies showed improved accuracy of p...

Deep learning model for prediction of extended-spectrum beta-lactamase (ESBL) production in community-onset Enterobacteriaceae bacteraemia from a high ESBL prevalence multi-centre cohort.

Adequate empirical antimicrobial coverage is instrumental in clinical management of community-onset Enterobacteriaceae bacteraemia in areas with high ...