AIMC Topic: Inpatients

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

Internal medicine journal Oct 27, 2021
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...
Machine Learning Forecasting Databases, Factual Length of Stay Humans Inpatients
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Modeling and prediction of pressure injury in hospitalized patients using artificial intelligence.

BMC medical informatics and decision making Aug 30, 2021
BACKGROUND: Hospital-acquired pressure injuries (PIs) induce significant patient suffering, inflate healthcare costs, and increase clinical co-morbidities. PIs are mostly due to bed-immobility, sensory impairment, bed positioning, and length of hospi...
Humans Neural Networks, Computer Artificial Intelligence Machine Learning Electronic Health Records Inpatients Pressure Ulcer
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Prediction of venous thromboembolism with machine learning techniques in young-middle-aged inpatients.

Scientific reports Jun 18, 2021
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...
Disease Susceptibility Machine Learning Adult Inpatients Male Humans Prognosis Area Under Curve Middle Aged Female Venous Thromboembolism Adolescent Young Adult Disease Management Support Vector Machine
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Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients.

JAMA network open Mar 1, 2021
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...
Humans Machine Learning Aged Young Adult Adult Male Middle Aged Anti-Bacterial Agents Retrospective Studies Electronic Health Records Female Child Follow-Up Studies Inpatients Antimicrobial Stewardship Risk Assessment Infant Adolescent Child, Preschool
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Automated estimation of echocardiogram image quality in hospitalized patients.

The international journal of cardiovascular imaging Nov 19, 2020
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...
Video Recording Image Interpretation, Computer-Assisted Automation Case-Control Studies Inpatients Predictive Value of Tests Feasibility Studies Reproducibility of Results Hospitalization Aged Aged, 80 and over Adult Male Echocardiography Middle Aged Respiration, Artificial Humans Machine Learning Female
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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 Sep 28, 2020
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...
Clinical Decision Rules Stroke Activities of Daily Living Stroke Rehabilitation Mobility Limitation Aged, 80 and over Male Middle Aged Risk Assessment Treatment Outcome Inpatients Decision Trees Databases, Factual Recovery of Function Predictive Value of Tests Humans Female Machine Learning Reproducibility of Results Risk Factors Aged
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A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.

PloS one Sep 22, 2020
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...
Middle Aged Betacoronavirus Retrospective Studies Coronavirus Infections Mass Screening Humans Pneumonia, Viral Inpatients Los Angeles SARS-CoV-2 Machine Learning Area Under Curve COVID-19 Testing Aged Pandemics Polymerase Chain Reaction Adult COVID-19 Clinical Laboratory Techniques
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Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model.

The American journal of medicine Aug 15, 2020
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...
Female Risk Factors Adolescent Aged Adult Humans Aged, 80 and over Hospital Mortality Male Young Adult Inpatients Machine Learning Middle Aged Big Data
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Caregiver burden in stroke inpatients: a randomized study comparing robot-assisted gait training and conventional therapy.

Acta neurologica Belgica Aug 10, 2020
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...
Exercise Therapy Stroke Rehabilitation Caregiver Burden Recovery of Function Robotics Stroke Gait Adult Male Humans Middle Aged Female Inpatients
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Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.

Journal of medical Internet research Aug 4, 2020
BACKGROUND: Detecting bacteremia among surgical in-patients is more obscure than other patients due to the inflammatory condition caused by the surgery. The previous criteria such as systemic inflammatory response syndrome or Sepsis-3 are not availab...
Humans Neural Networks, Computer Bacteremia Retrospective Studies Inpatients Female Male Deep Learning
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