The study aims to overview patients registered to Bezmialem Vakıf University Hospital Intensive Care Unit after successive cardiac arrest resuscitation from October 2010 to September 2025. The goal is to determine length of stay in reanimation, neurological clinical outcome and costs of these patients at discharge from the department. All these data is intended to be evaluated by artificial intelligence to evaluate a predictive model.
age>18 years
successive cardiopulmonary resuscitation
at least 1 hour long admission to ICU after Return Of Spontaneous Circulation (ROSC)
age < 18 years
>80% missing data in patient records
patients with no ROSC
Patients above 18 years after successful cardiopulmonary resuscitation with ROSC registered at Bezmialem Vakıf University Hospital Intensive care unit from October 2010 to September 2025 will be included in the resesarch.
Cohort
Retrospective
no physical or medical interventions
Other interventionData from patients after successive rescucitaion will be evaluated by machine learning programs.
Machine Learning Python programme
The created database will be analyzed using a machine learning artificial intelligence algorithm with the Python programming language. After processing missing and incomplete data by artificial intelligence, the database will be divided into two parts: model training and model validation. Meaningful data will be selected through model training, and a prediction model will be built based on these data. To increase the interpretability of the prediction model and help users understand how and why certain predictions are made, the SHapley Additive exPlanations (SHAP) algorithm will be used. In machine learning, the SHAP technique is used to interpret the decision-making processes of complex machine learning models.
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