AI Medical Compendium Topic

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Hospitals

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Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation.

Medical care
OBJECTIVE: To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation.

Machine learning to predict stroke risk from routine hospital data: A systematic review.

International journal of medical informatics
PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk stratification tools such as CHADS-VASc and QRISK3 are of limited accuracy, particularly in those without a diagnosis of atrial-fibrillation. Hence, there ...

Using Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed.

Sensors (Basel, Switzerland)
In hospitals, timely interventions can prevent avoidable clinical deterioration. Early recognition of deterioration is vital to stopping further decline. Measuring the way patients position themselves in bed and change their positions may signal when...

Social Determinants and Health Equity Activities: Are They Connected with the Adaptation of AI and Telehealth Services in the U.S. Hospitals?

International journal of environmental research and public health
In recent decades, technological shifts within the healthcare sector have significantly transformed healthcare management and utilization, introducing unprecedented possibilities that elevate quality of life. Organizational factors are recognized as ...

Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, pro...

Harnessing artificial intelligence for infection control and prevention in hospitals: A comprehensive review of current applications, challenges, and future directions.

Saudi medical journal
Hospital-acquired infections (HAIs) significantly burden global healthcare systems, exacerbated by antibiotic-resistant bacteria. Traditional infection control measures often lack consistency due to variable human compliance. This comprehensive revie...

Unraveling relevant cross-waves pattern drifts in patient-hospital risk factors among hospitalized COVID-19 patients using explainable machine learning methods.

BMC infectious diseases
BACKGROUND: Several studies explored factors related to adverse clinical outcomes among COVID-19 patients but lacked analysis of the impact of the temporal data shifts on the strength of association between different predictors and adverse outcomes. ...

Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms.

IEEE journal of biomedical and health informatics
In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) ...

Exploring Suitability of Low-Severity Rating Hospital Incident Reports for Machine Learning.

Computers, informatics, nursing : CIN
Electronic incident reporting is a key quality and a safety process for healthcare organizations that assists in evaluating performance and informing quality improvement initiatives. Although it is mandatory for high-severity incident reports to be i...

LeFood-set: Baseline performance of predicting level of leftovers food dataset in a hospital using MT learning.

PloS one
Monitoring the remaining food in patients' trays is a routine activity in healthcare facilities as it provides valuable insights into the patients' dietary intake. However, estimating food leftovers through visual observation is time-consuming and bi...