Machine Learning Based Prediction of Platelet Concentration from ROTEM Measurements
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 2,500
- 试验地点
- 1
- 主要终点
- Predicition of platelet conentration from ROTEM measurements using machine learning
研究概览
简要总结
Viscoelastic testing is a highly recommended cornerstone of modern coagulation medicine, reducing transfusion needs. A disadvantage of viscoelastic tests is the impossibility of making a definitive statement about the platelet count.
Therefore, the aim of this retrospective observational study is, on the one hand, to predict the platelet count based on standard ROTEM parameters with the help of several machine learning methods and, on the other hand, to detect a low platelet count ( <100000 ml-1 and < 50000 ml-1).
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 100 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •ROTEM measurement and platelet count measurement within 3 hours.
排除标准
- •under 18 Years
- •more than 3 hours between ROTEM and platelet count measurement
结局指标
主要结局
Predicition of platelet conentration from ROTEM measurements using machine learning
时间窗: Obtained ROTEM analyses are the baseline at all four centres and patients will be included if platelets were determined concomitantly within three hours on the same day.
Several machine learning techniques for the prediction of the platelet concentration from ROTEM parameters (regression approach), namely linear regression, Random Forest, neural network, gradient boosting machine (GBM) and adaptive boosting (ADA) will be assessed. Describing the quality of these prediction models, the mean square error (MSE), the root of the mean of the square of errors(RMSE), the mean absolute error (MAE), and the root mean squared logarithmic error (RMSLE), and the coefficient of determination (R2) will be used.
次要结局
未报告次要终点