Machine learning survival model for personalised prevention of catheter-related thrombosis in tumour patients

The high c-indexes based on crank predictions demonstrated the ability of the SM-CRT model to identify high-risk patients for thromboprophylaxis. Additionally, the SM-CRT model can guide extubation time by identifying high-risk periods through distr predictions.