AI Medical Compendium Topic:
Intensive Care Units

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Precision Intensive Care: A Real-Time Artificial Intelligence Strategy for the Future.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies

Evaluating ICU Clinical Severity Scoring Systems and Machine Learning Applications: APACHE IV/IVa Case Study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Clinical scoring systems have been developed for many specific applications, yet they remain underutilized for common reasons such as model inaccuracy and difficulty of use. For intensive care units specifically, the Acute Physiology and Chronic Heal...

Predicting Mortality in the Surgical Intensive Care Unit Using Artificial Intelligence and Natural Language Processing of Physician Documentation.

The American surgeon
The purpose of this study was to use natural language processing of physician documentation to predict mortality in patients admitted to the surgical intensive care unit (SICU). The Multiparameter Intelligent Monitoring in Intensive Care III database...

Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data.

Annals of the American Thoracic Society
RATIONALE: Patients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving...

Inclusion of Unstructured Clinical Text Improves Early Prediction of Death or Prolonged ICU Stay.

Critical care medicine
OBJECTIVES: Early prediction of undesired outcomes among newly hospitalized patients could improve patient triage and prompt conversations about patients' goals of care. We evaluated the performance of logistic regression, gradient boosting machine, ...

Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients.

Critical care medicine
OBJECTIVES: Risk adjustment algorithms for ICU mortality are necessary for measuring and improving ICU performance. Existing risk adjustment algorithms are not widely adopted. Key barriers to adoption include licensing and implementation costs as wel...

[Application of support vector machine in predicting in-hospital mortality risk of patients with acute kidney injury in ICU].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
OBJECTIVE: To construct an in-hospital mortality prediction model for patients with acute kidney injury (AKI) in intensive care unit (ICU) by using support vector machine (SVM), and compare it with the simplified acute physiology score II (SAPS-II) w...