AIMC Topic: Intensive Care Units

Clear Filters Showing 241 to 250 of 694 articles

Artificial intelligence to predict bed bath time in Intensive Care Units.

Revista brasileira de enfermagem
OBJECTIVES: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients.

Opening Pandora's box by generating ICU diaries through artificial intelligence: A hypothetical study protocol.

Intensive & critical care nursing
BACKGROUND: Patients and families on Intensive Care Units (ICU) benefit from ICU diaries, enhancing their coping and understanding of their experiences. Staff shortages and a limited amount of time severely restrict the application of ICU diaries. To...

INTERPRETABLE MACHINE LEARNING FOR PREDICTING RISK OF INVASIVE FUNGAL INFECTION IN CRITICALLY ILL PATIENTS IN THE INTENSIVE CARE UNIT: A RETROSPECTIVE COHORT STUDY BASED ON MIMIC-IV DATABASE.

Shock (Augusta, Ga.)
The delayed diagnosis of invasive fungal infection (IFI) is highly correlated with poor prognosis in patients. Early identification of high-risk patients with invasive fungal infections and timely implementation of targeted measures is beneficial for...

Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study.

European journal of clinical investigation
BACKGROUND: Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer th...

Neuromonitoring in the ICU - what, how and why?

Current opinion in critical care
PURPOSE OF REVIEW: We selectively review emerging noninvasive neuromonitoring techniques and the evidence that supports their use in the ICU setting. The focus is on neuromonitoring research in patients with acute brain injury.

Robot-assisted early mobilization for intensive care unit patients: Feasibility and first-time clinical use.

International journal of nursing studies
BACKGROUND: Early mobilization is only carried out to a limited extent in the intensive care unit. To address this issue, the robotic assistance system VEMOTION® was developed to facilitate (early) mobilization measures more easily. This paper descri...

Machine learning methods for developing a predictive model of the incidence of delirium in cardiac intensive care units.

Revista espanola de cardiologia (English ed.)
INTRODUCTION AND OBJECTIVES: Delirium, recognized as a crucial prognostic factor in the cardiac intensive care unit (CICU), has evolved in response to the changing demographics among critically ill cardiac patients. This study aimed to create a predi...

Cross-institution natural language processing for reliable clinical association studies: a methodological exploration.

Journal of clinical epidemiology
OBJECTIVES: Natural language processing (NLP) of clinical notes in electronic medical records is increasingly used to extract otherwise sparsely available patient characteristics, to assess their association with relevant health outcomes. Manual data...

Measuring Implicit Bias in ICU Notes Using Word-Embedding Neural Network Models.

Chest
BACKGROUND: Language in nonmedical data sets is known to transmit human-like biases when used in natural language processing (NLP) algorithms that can reinforce disparities. It is unclear if NLP algorithms of medical notes could lead to similar trans...