An accurate model of a patient’s individual survival distribution can help determine the appropriate treatment for terminal patients. Unfortunately, risk scores (for example from Cox Proportional Hazard models) do not provide survival probabilities, single-time probability models (for instance the Gail model, predicting 5 year probability) only provide for a single time point, and standard Kaplan-Meier survival curves provide only population averages for a large class of patients, meaning they are not specific to individual patients. This motivates an alternative class of tools that can learn a model that provides an individual survival distribution for each subject, which gives survival probabilities across all times, such as extensions to the Cox model, Accelerated Failure Time, an extension to Random Survival Forests, and Multi-Task Logistic Regression. This paper first motivates such 'individual survival distribution' (ISD) models, and explains how they differ from standard models. It then discusses ways to evaluate such models — namely Concordance, 1-Calibration, Integrated Brier score, and versions of L1-loss — then motivates and defines a novel approach, 'D-Calibration', which determines whether a model's probability estimates are meaningful. We also discuss how these measures differ, and use them to evaluate several ISD prediction tools over a range of survival data sets. We also provide a code base for all of these survival models and evaluation measures, at https://github.com/haiderstats/ISDEvaluation.

We explore several ways to estimate movie similarity from the free encyclopedia Wikipedia with the goal of im-proving our predictions for the Netflix Prize. Our system first uses the content and hyperlink structure of Wikipedia articles to identify similarities between movies. We then predict a user’s unknown ratings by using these similarities in conjunction with the user’s known ratings to initialize matrix factorization and k-Nearest Neighbours algorithms. We blend these results with existing ratings-based predic-tors. Finally, we discuss our empirical results, which sug-gest that external Wikipedia data does not significantly im-prove the overall prediction accuracy. 1