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Iatrogenic Disease

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Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Hospital-acquired pressure injuries are a serious problem among critical care patients. Some can be prevented by using measures such as specialty beds, which are not feasible for every patient because of costs. However, decisions about wh...

Ripe for Disruption? Adopting Nurse-Led Data Science and Artificial Intelligence to Predict and Reduce Hospital-Acquired Outcomes in the Learning Health System.

Nursing administration quarterly
Nurse leaders are dually responsible for resource stewardship and the delivery of high-quality care. However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude. Although hospitals and health systems have b...

A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Reliable longitudinal risk prediction for hospitalized patients is needed to provide quality care. Our goal is to develop a generalizable model capable of leveraging clinical notes to predict healthcare-associated diseases 24-96 hours in a...

Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016-2018.

BMJ open
PURPOSE: In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the ...

Predictive analytics for early detection of hospital-acquired complications: An artificial intelligence approach.

Health information management : journal of the Health Information Management Association of Australia
BACKGROUND: Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs.

Deep Learning-Based Real-Time Ureter Identification in Laparoscopic Colorectal Surgery.

Diseases of the colon and rectum
BACKGROUND: Iatrogenic ureteral injury is a serious complication of abdominopelvic surgery. Identifying the ureters intraoperatively is essential to avoid iatrogenic ureteral injury. We developed a model that may minimize this complication.

Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) ...