AIMC Topic: Hospital Mortality

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Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mellitus.

Acta cardiologica
BACKGROUND: Predicting the prognosis of patients with acute myocardial infarction (AMI) combined with diabetes mellitus (DM) is crucial due to high in-hospital mortality rates. This study aims to develop and validate a mortality risk prediction model...

A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

Pediatric nephrology (Berlin, Germany)
BACKGROUND: Intradialytic hypotension (IDH) is associated with mortality in adults undergoing intermittent hemodialysis, but this relationship is unclear in critically ill children receiving continuous kidney replacement therapy (CKRT). We aim to eva...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

Nature medicine
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pr...

Prognostic value of SAPS II score for 28-day mortality in ICU patients with acute pulmonary embolism.

International journal of cardiology
BACKGROUND: Acute pulmonary embolism (APE) is a common and life-threatening emergency in intensive care units (ICUs). Effective risk assessment tools are essential to improve patient outcomes. This study aims to evaluate the association between Simpl...

Development and validation of inpatient mortality prediction models for patients with hyperglycemic crisis using machine learning approaches.

BMC endocrine disorders
BACKGROUND: Hyperglycemic crisis is one of the most common and severe complications of diabetes mellitus, associated with a high motarlity rate. Emergency admissions due to hyperglycemic crisis remain prevalent and challenging. This study aimed to de...

Machine learning-based model for predicting all-cause mortality in severe pneumonia.

BMJ open respiratory research
BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians i...

Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.

Scientific reports
Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable machine learning (ML) model for predicting in-hospital mortality of CA patients who survived more than 7...

Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU.

International journal of medical informatics
BACKGROUND: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications ar...

Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis.

International journal of medical informatics
BACKGROUND: Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, th...