Hospital-Based Medicine

Latest AI and machine learning research in hospital-based medicine for healthcare professionals.

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Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing...

Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards.

BACKGROUND: Large amounts of patient data are routinely manually collected in hospitals by using sta...

Machine Learning for Outcome Prediction in Electroencephalograph (EEG)-Monitored Children in the Intensive Care Unit.

The aim of this study was to evaluate the performance of models predicting in-hospital mortality in ...

Children's Imaginaries of Human-Robot Interaction in Healthcare.

This paper analyzes children’s imaginaries of Human-Robots Interaction (HRI) in the context of...

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clini...

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Seizure prediction has attracted growing attention as one of the most challenging predictive data an...

Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.

BACKGROUND: Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data...

DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning.

Electronic health records have brought valuable improvements to hospital practices by integrating pa...

Predicting Hospital Readmission via Cost-Sensitive Deep Learning.

With increased use of electronic medical records (EMRs), data mining on medical data has great poten...

Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

De-identification of clinical notes is a special case of named entity recognition. Supervised machin...

Calibration Drift Among Regression and Machine Learning Models for Hospital Mortality.

Advanced regression and machine learning models can provide personalized risk predictions to support...

Assessing patient risk of central line-associated bacteremia via machine learning.

BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidi...

Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.

BACKGROUND: Various technologies have been developed to improve hand hygiene (HH) compliance in inpa...

Intravenous infusion of magnesium sulfate is not associated with cardiovascular, liver, kidney, and metabolic toxicity in adults.

BACKGROUND: Magnesium (Mg) deficiency contributes to the pathophysiology of numerous diseases. The t...

A survey on Barrett's esophagus analysis using machine learning.

This work presents a systematic review concerning recent studies and technologies of machine learnin...

Overproduction of efflux pumps caused reduced susceptibility to carbapenem under consecutive imipenem-selected stress in .

PURPOSE: is an important pathogen in the nosocomial infections worldwide. Combining with carbapenem...

Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients.

Ischemic stroke is a leading cause of disability and death worldwide among adults. The individual pr...

Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier.

Electronic health records (EHRs) contain critical information useful for clinical studies. Early ass...

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, commun...

Palliative analgesia with topical sevoflurane in cancer-related skin ulcers: a case report.

A Caucasian 39-year-old male patient with a poorly-differentiated infiltrating epidermoid penile car...

Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data.

Electronic Health Records (EHR) are mainly designed to record relevant patient information during th...

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