AIMC Topic: Hospital Mortality

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Early heart rate variability evaluation enables to predict ICU patients' outcome.

Scientific reports
Heart rate variability (HRV) is a mean to evaluate cardiac effects of autonomic nervous system activity, and a relation between HRV and outcome has been proposed in various types of patients. We attempted to evaluate the best determinants of such var...

Developing machine learning models for prediction of mortality in the medical intensive care unit.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alert of patient deterioration is essential for prompt medical intervention in the Medical Intensive Care Unit (MICU). Logistic Regression (LR) has been used for the development of most conventional severity-of-illness scori...

Machine Learning Approaches-Driven for Mortality Prediction for Patients Undergoing Craniotomy in ICU.

Brain injury
OBJECTIVES: We aimed to predict the mortality of patients with craniotomy in ICU by using predictive models to extract the high-risk factors leading to the death of patients from a retrospective a study.

Anemia or other comorbidities? using machine learning to reveal deeper insights into the drivers of acute coronary syndromes in hospital admitted patients.

PloS one
Acute coronary syndromes (ACS) are a leading cause of deaths worldwide, yet the diagnosis and treatment of this group of diseases represent a significant challenge for clinicians. The epidemiology of ACS is extremely complex and the relationship betw...

Improving the In-Hospital Mortality Prediction of Diabetes ICU Patients Using a Process Mining/Deep Learning Architecture.

IEEE journal of biomedical and health informatics
Diabetes intensive care unit (ICU) patients are at increased risk of complications leading to in-hospital mortality. Assessing the likelihood of death is a challenging and time-consuming task due to a large number of influencing factors. Healthcare p...

Evaluating pointwise reliability of machine learning prediction.

Journal of biomedical informatics
Interest in Machine Learning applications to tackle clinical and biological problems is increasing. This is driven by promising results reported in many research papers, the increasing number of AI-based software products, and by the general interest...

Machine Learning-Based Mortality Prediction of Patients at Risk During Hospital Admission.

Journal of patient safety
OBJECTIVES: The ability to predict in-hospital mortality from data available at hospital admission would identify patients at risk and thereby assist hospital-wide patient safety initiatives. Our aim was to use modern machine learning tools to predic...

Machine learning for emerging infectious disease field responses.

Scientific reports
Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very sh...

Unstructured clinical notes within the 24 hours since admission predict short, mid & long-term mortality in adult ICU patients.

PloS one
Mortality prediction for intensive care unit (ICU) patients is crucial for improving outcomes and efficient utilization of resources. Accessibility of electronic health records (EHR) has enabled data-driven predictive modeling using machine learning....

Development of a prediction score for in-hospital mortality in COVID-19 patients with acute kidney injury: a machine learning approach.

Scientific reports
Acute kidney injury (AKI) is frequently associated with COVID-19 and it is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score)....