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Survival prediction with small sets of features is a highly relevant topicfor decision-making in clinical practice. I describe a method for predictingsurvival of amyotrophic lateral sclerosis (ALS) patients that was developed asa submission to the DREAM ALS Stratification Prize4Life Challenge held insummer 2015 to find the most accurate prediction of ALS progression andsurvival. ALS is a neurodegenerative disease with very heterogeneous survivaltimes. Based on patient data from two national registries, solvers were askedto predict survival for three different time intervals, which was thenevaluated on undisclosed information from additional data. I describe methodsused to generate new features from existing ones from longitudinal data,selecting the most predictive features, and developing the best survival model.I show that easily obtainable engineered features can significantly improveprediction and could be incorporated into clinical practice. Furthermore, myprediction model confirms previous reports suggesting that past diseaseprogression measured by the ALSFRS (ALS functional rating scale score), timesince disease onset, onset site, and age are strong predictors for survival.Regarding prediction accuracy, this approach ranked second.

Survival Prediction with Limited Features: a Top Performing Approach from the DREAM ALS Stratification Prize4Life Challenge