AIMC Topic: Hospital Mortality

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Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset.

JMIR medical informatics
BACKGROUND: Reusing long-term data from electronic health records is essential for training reliable and effective health artificial intelligence (AI). However, intrinsic changes in health data distributions over time-known as dataset shifts, which i...

Using machine learning for early prediction of in-hospital mortality during ICU admission in liver cancer patients.

Scientific reports
Liver cancer has a high incidence and mortality rate globally, particularly in patients requiring intensive care unit (ICU) admission. Early prediction of in-hospital mortality for these patients is crucial, yet lacking reliable tools. This study aim...

Performance of the pediatric index of mortality (PIM-3) in a Moroccan PICU: challenges in resource-limited settings.

European journal of pediatrics
UNLABELLED: Prognostic scores such as the Pediatric Index of Mortality (PIM-3) are widely used to estimate mortality risk in PICUs, yet their performance in low- and middle-income countries (LMICs) remains uncertain. We aimed to evaluate the predicti...

Impact of blood culture positivity at intensive care unit admission on mortality in infective endocarditis: Machine learning and deep learning-based causal inference models.

PloS one
BACKGROUND: Infective endocarditis (IE) carries high in-hospital mortality, particularly among intensive care unit (ICU) patients. The predictive role of blood culture positivity in these patients remains unclear.

Machine learning models predict mortality risk in diabetic neuropathy patients using MIMIC-IV data.

Scientific reports
We aimed to construct and validate interpretable models for predicting mortality risk using machine learning (ML) methods to identify the risk factors associated with mortality in patients with diabetic neuropathy (DN). We selected patients from the ...

Decoding dynamic lipase trajectory patterns and in-hospital mortality in acute pancreatitis: insights from machine learning in intensive care units.

European journal of medical research
BACKGROUND: Serum lipase levels are crucial biomarkers in acute pancreatitis (AP), yet their dynamic patterns and prognostic implications remain incompletely understood. This study aimed to identify distinct lipase trajectory phenotypes and evaluate ...

Exploring the therapeutic effects of continuous kidney replacement therapy in patients with severe acidosis using deep learning-based causal inference.

Scientific reports
Continuous kidney replacement therapy (CKRT) is an essential treatment for uncontrolled severe metabolic acidosis. However, CKRT can increase workload and lead to complications, thus necessitating its selective application to patients who stand to be...

Development and external validation of an artificial intelligence model for predicting mortality and prolonged ICU stay in postoperative critically ill patients: a retrospective study.

World journal of emergency surgery : WJES
BACKGROUND: Existing predictive models in critical care, specifically for postoperative critically ill patients, often struggle to accurately predict prolonged intensive care unit (ICU) stays, a key aspect of patient care. The integration of artifici...

Hospital Outcome of Host Heterogeneity, Organ dysfunction and Trajectory in sepsis (HOHHOT): A cohort study in the critical care unit.

BMJ open
INTRODUCTION: Prognosis estimation is the basis for establishing the personal interventions in sepsis patients. Serum biomarkers are potential tools for predicting the outcomes of sepsis patients admitted to the intensive care unit (ICU). Here, we pl...