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Inpatients

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A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.

PloS one
Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient settin...

Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model.

The American journal of medicine
BACKGROUND: General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learnin...

Caregiver burden in stroke inpatients: a randomized study comparing robot-assisted gait training and conventional therapy.

Acta neurologica Belgica
The effects of caregiver burden during the inpatient rehabilitation period have not yet been investigated. The purpose of this study was to evaluate the burden on stroke survivors' caregivers during the inpatient rehabilitation period, and to compare...

Development and Validation of Machine Learning-Based Prediction for Dependence in the Activities of Daily Living after Stroke Inpatient Rehabilitation: A Decision-Tree Analysis.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND AND PURPOSE: Accurate prediction using simple and changeable variables is clinically meaningful because some known-predictors, such as stroke severity and patients age cannot be modified with rehabilitative treatment. There are limited cli...

Automated estimation of echocardiogram image quality in hospitalized patients.

The international journal of cardiovascular imaging
We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient grou...

Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients.

JAMA network open
IMPORTANCE: Comparisons of antimicrobial use among hospitals are difficult to interpret owing to variations in patient case mix. Risk-adjustment strategies incorporating larger numbers of variables haves been proposed as a method to improve compariso...

Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.

Scientific reports
Accumulating studies appear to suggest that the risk factors for venous thromboembolism (VTE) among young-middle-aged inpatients are different from those among elderly people. Therefore, the current prediction models for VTE are not applicable to you...

Machine learning in the prediction of medical inpatient length of stay.

Internal medicine journal
Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previ...

Development and usability evaluation of a bedside robot system for inpatients.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Many inpatients become anxious or frightened about scheduled treatment processes, and medical staff do not have sufficient time to provide emotional support. The recent advancement of information and communications technology (ICT) and th...