Hospital-Based Medicine

Robotic dispensing improves patient safety, inventory management, and staff satisfaction in an outpatient hospital pharmacy.

RATIONALE, AIMS AND OBJECTIVES: Implementation of robotic systems in outpatient hospital pharmacies is uncommon. Other than cost, 1 of the barriers to widespread adoption is the lack of definitive evidence that this technology actually reduces dispensing errors and improves inventory management.

Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.

OBJECTIVE: Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supplementary means of risk prediction is from the automated analysis of case notes in Electronic Health Records (EHRs) using Natural Language Processing (NLP). This exploratory study rated presence or absence and frequency of words in a forensic EHR dat...

Big data and machine learning in critical care: Opportunities for collaborative research.

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready...

RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.

BACKGROUND: Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both repro...

Predicting hospital admission at emergency department triage using machine learning.

OBJECTIVE: To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.

Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reas...

Optimizing Autoencoders for Learning Deep Representations From Health Data.

Analyzing patients' health data using machine learning techniques can improve both patient outcomes and hospital operations. However, heterogeneous pa...

Molecular Genotyping of Species Isolated from Patients in Tehran, Iran, by Repetitive Element PCR Fingerprinting.

BACKGROUND & OBJECTIVE: is an opportunistic pathogen with high pathogenic and antibiotic-resistance potential and is also considered as one of the ma...

Decontamination of collards ( var. L.) using electrolyzed water and corona discharge plasma jet.

Acidic electrolyzed water (AEW) was used for collards sanitization. In the AEW (pH of 3.6; 230 mg/L chlorine) generator, the rates of brine inflow and...

Adverse Outcomes due to Aggressive Fluid Resuscitation in Children: A Prospective Observational Study.

Fluid management has a major impact on the duration, severity, and outcome of critically ill children. The aim of this study was to examine the relati...

A computer-based approach for data analyzing in hospital's health-care waste management sector by developing an index using consensus-based fuzzy multi-criteria group decision-making models.

BACKGROUND: Proper Health-Care Waste Management (HCWM) and integrated documentation in this sector of hospitals require analyzing massive data collect...

Machine learning detects EEG microstate alterations in patients living with temporal lobe epilepsy.

PURPOSE: Quasi-stable electrical distribution in EEG called microstates could carry useful information on the dynamics of large scale brain networks. ...

[Expert systems-urgently needed for early diagnosis of sepsis : Results of retrospective clinical validation of the expert system FLORIDA].

The expert system FLORIDA (Fuzzy Logic Orientated Rule Interpreter for Diagnostic Applications) is equipped with a knowledge base applying linguistic ...

An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest.

BACKGROUND: In-hospital cardiac arrest is a major burden to public health, which affects patient safety. Although traditional track-and-trigger system...

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data.

BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, mana...

Mapping the transmission risk of Zika virus using machine learning models.

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expan...

Time series model for forecasting the number of new admission inpatients.

BACKGROUND: Hospital crowding is a rising problem, effective predicting and detecting managment can helpful to reduce crowding. Our team has successfu...

Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly addr...

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 critically ill children undergoing continuous elec...

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

This paper analyzes children’s imaginaries of Human-Robots Interaction (HRI) in the context of social robots in healthcare, and it explores ethi...