Conformal Prediction Using Random Survival Forests

Author(s):  
Henrik Bostrom ◽  
Lars Asker ◽  
Ram Gurung ◽  
Isak Karlsson ◽  
Tony Lindgren ◽  
...  
Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2442
Author(s):  
Moniek van Zutphen ◽  
Fränzel J. B. van Duijnhoven ◽  
Evertine Wesselink ◽  
Ruud W. M. Schrauwen ◽  
Ewout A. Kouwenhoven ◽  
...  

Current lifestyle recommendations for cancer survivors are the same as those for the general public to decrease their risk of cancer. However, it is unclear which lifestyle behaviors are most important for prognosis. We aimed to identify which lifestyle behaviors were most important regarding colorectal cancer (CRC) recurrence and all-cause mortality with a data-driven method. The study consisted of 1180 newly diagnosed stage I–III CRC patients from a prospective cohort study. Lifestyle behaviors included in the current recommendations, as well as additional lifestyle behaviors related to diet, physical activity, adiposity, alcohol use, and smoking were assessed six months after diagnosis. These behaviors were simultaneously analyzed as potential predictors of recurrence or all-cause mortality with Random Survival Forests (RSFs). We observed 148 recurrences during 2.6-year median follow-up and 152 deaths during 4.8-year median follow-up. Higher intakes of sugary drinks were associated with increased recurrence risk. For all-cause mortality, fruit and vegetable, liquid fat and oil, and animal protein intake were identified as the most important lifestyle behaviors. These behaviors showed non-linear associations with all-cause mortality. Our exploratory RSF findings give new ideas on potential associations between certain lifestyle behaviors and CRC prognosis that still need to be confirmed in other cohorts of CRC survivors.


2017 ◽  
pp. 113-113
Author(s):  
Freshteh osmani ◽  
Atefeh hajian ◽  
Ebrahim hajizadeh

2011 ◽  
Vol 4 (1) ◽  
pp. 115-132 ◽  
Author(s):  
Hemant Ishwaran ◽  
Udaya B. Kogalur ◽  
Xi Chen ◽  
Andy J. Minn

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Joeri Ruyssinck ◽  
Joachim van der Herten ◽  
Rein Houthooft ◽  
Femke Ongenae ◽  
Ivo Couckuyt ◽  
...  

Predicting the bed occupancy of an intensive care unit (ICU) is a daunting task. The uncertainty associated with the prognosis of critically ill patients and the random arrival of new patients can lead to capacity problems and the need for reactive measures. In this paper, we work towards a predictive model based on Random Survival Forests which can assist physicians in estimating the bed occupancy. As input data, we make use of the Sequential Organ Failure Assessment (SOFA) score collected and calculated from 4098 patients at two ICU units of Ghent University Hospital over a time period of four years. We compare the performance of our system with a baseline performance and a standard Random Forest regression approach. Our results indicate that Random Survival Forests can effectively be used to assist in the occupancy prediction problem. Furthermore, we show that a group based approach, such as Random Survival Forests, performs better compared to a setting in which the length of stay of a patient is individually assessed.


2016 ◽  
Vol 49 (11) ◽  
pp. 562-569 ◽  
Author(s):  
Sergii Voronov ◽  
Daniel Jung ◽  
Erik Frisk

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