AI Medical Compendium Topic

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Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.

Frontiers in public health
This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to intensive care units (ICUs). A total of 2,517 patients treated in the ICU of a tertiary hospital in Ch...

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.

Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

Antimicrobial resistance and infection control
BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO p...

Risk assessment and prediction of nosocomial infections based on surveillance data using machine learning methods.

BMC public health
BACKGROUND: Nosocomial infections with heavy disease burden are becoming a major threat to the health care system around the world. Through long-term, systematic, continuous data collection and analysis, Nosocomial infection surveillance (NIS) system...

A Vision on User-Centered Implementation and Evaluation of Explainable AI for Predicting Hospital-Onset Bacteremia.

Studies in health technology and informatics
In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algo...

Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis.

Antimicrobial resistance and infection control
BACKGROUND: Nosocomial infections (NIs) frequently occur and adversely impact prognosis for hospitalized patients with cirrhosis. This study aims to develop and validate two machine learning models for NIs and in-hospital mortality risk prediction.

Integration of an electronic hand hygiene auditing system with electronic health records using machine learning to predict hospital-acquired infection in a health care setting.

American journal of infection control
BACKGROUND: Hospital-acquired infections (HAIs) increase morbidity, mortality, and health care costs. Effective hand hygiene (HH) is crucial for prevention, but achieving high compliance remains challenge. This study explores using machine learning t...

Development and validation of a sepsis risk index supporting early identification of ICU-acquired sepsis: an observational study.

Anaesthesia, critical care & pain medicine
BACKGROUND: Sepsis is a threat to global health, and domestically is the major cause of in-hospital mortality. Due to increases in inpatient morbidity and mortality resulting from sepsis, healthcare providers (HCPs) would accrue significant benefits ...