AN ARTIFICIAL INTELLIGENCE MODEL FOR INTENSIVE CARE LENGTH OF STAY, NEUROLOGICAL OUTCOME AND COSTS ESTIMATION AFTER CARDIOPULMONARY RESUSCITATION: A COHORT STUDY
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 5,000
- 主要终点
- Machine Learning Python programme
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •age>18 years
- •successive cardiopulmonary resuscitation
- •at least 1 hour long admission to ICU after Return Of Spontaneous Circulation (ROSC)
排除标准
- •age < 18 years
- •>80% missing data in patient records
- •patients with no ROSC
结局指标
主要结局
Machine Learning Python programme
时间窗: 3 months
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.
次要结局
未报告次要终点
研究者
Nıgar Kangarlı
Uzman Doctor
Bezmialem Vakif University