Hospital-Based Medicine

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 clinical, demographic and neuroimaging data from two independent initiatives (University of Michigan and Tel Aviv Sourasky Medical Center). Using machine learning techniques, we construct predictive models to discriminate fallers and non-fallers. Through controlled feature selection, we identified the mo...

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 analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved hi...

Evolution of upper limb kinematics four years after subacute robot-assisted rehabilitation in stroke patients.

To assess functional status and robot-based kinematic measures four years after subacute robot-assisted rehabilitation in hemiparesis. Twenty-two pa...

DDC-Outlier: Preventing Medication Errors Using Unsupervised Learning.

Electronic health records have brought valuable improvements to hospital practices by integrating patient information. In fact, the understanding of t...

Predicting Hospital Readmission via Cost-Sensitive Deep Learning.

With increased use of electronic medical records (EMRs), data mining on medical data has great potential to improve the quality of hospital treatment ...

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 machine-learning (ML) algorithms have achieved promising...

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

BACKGROUND: Central line-associated bloodstream infections (CLABSIs) contribute to increased morbidity, length of hospital stay, and cost. Despite pro...

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 inpatient settings; however, little is known about the...

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 therapeutic use of Mg has steadily increased over t...

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

This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treat...

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 carbapenemases, efflux pumps and outer membrane proteins (OM...

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 prognosis after stroke is extremely dependent on tre...

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 assessment of patients' mortality in intensive care u...

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, communication, cognitive, and social skills and abilitie...

Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.

Purpose To compare different methods for generating features from radiology reports and to develop a method to automatically identify findings in thes...

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 carcinoma with urethral invasion was diagnosed. The p...

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 their stay in the hospital for administrative purpos...

An ensemble boosting model for predicting transfer to the pediatric intensive care unit.

BACKGROUND: Early deterioration indicators have the potential to alert hospital care staff in advance of adverse events, such as patients requiring an...

Assessing the impact of PM on respiratory disease using artificial neural networks.

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM can penetrate th...

How Confounder Strength Can Affect Allocation of Resources in Electronic Health Records.

When electronic health record (EHR) data are used, multiple approaches may be available for measuring the same variable, introducing potentially confo...