An accurate model of patient survival time can help in the treatment and care of cancer patients. The common practice of providing survival time estimates based only on population averages for the site and stage of cancer ignores many important individual differences among patients. In this paper, we propose a local regression method for learning patient-specific survival time distribution based on patient attributes such as blood tests and clinical assessments. When tested on a cohort of more than 2000 cancer patients, our method gives survival time predictions that are much more accurate than popular survival analysis models such as the Cox and Aalen regression models. Our results also show that using patient-specific attributes can reduce the prediction error on survival time by as much as 20% when compared to using cancer site and stage only.
Investing in a pre-packed 72-hour emergency preparedness kit, also known as a go-bag or bug-out bag, is a smart way to help ensure that, should you need to leave home during an emergency--such as an earthquake, flood, fire, or hurricane--you'll have most of what's needed to stay safe and healthy for up to three days. Seventy-two hours is the amount of time that many emergency management planners and government agencies say it could take for local and federal authorities to begin lending aid to people in the aftermath of a significant disaster. While it's better in almost all circumstances to create your own survival kit, buying a pre-packed go-bag is a great way to set you on the path to staying safe and healthy when things go very, very wrong. Relying on years of experience and after days of research and testing, we feel that the Urban Survival Bug Out Bag from Emergency Zone (available at Amazon) is the best 3-day survival kit for most people to invest in. Despite the fact that this kit is said to be suitable for two people, we feel that, based on our research, it is better suited for use by a single individual.
Kernel Induced Random Survival Forests (KIRSF) is a statistical learning algorithm which aims to improve prediction accuracy for survival data. As in Random Survival Forests (RSF), Cumulative Hazard Function is predicted for each individual in the test set. Prediction error is estimated using Harrell's concordance index (C index) [Harrell et al. (1982)]. The C-index can be interpreted as a misclassification probability and does not depend on a single fixed time for evaluation. The C-index also specifically accounts for censoring. By utilizing kernel functions, KIRSF achieves better results than RSF in many situations. In this report, we show how to incorporate kernel functions into RSF. We test the performance of KIRSF and compare our method to RSF. We find that the KIRSF's performance is better than RSF in many occasions.
Reducing user attrition, i.e. churn, is a broad challenge faced by several industries. In mobile social games, decreasing churn is decisive to increase player retention and rise revenues. Churn prediction models allow to understand player loyalty and to anticipate when they will stop playing a game. Thanks to these predictions, several initiatives can be taken to retain those players who are more likely to churn. Survival analysis focuses on predicting the time of occurrence of a certain event, churn in our case. Classical methods, like regressions, could be applied only when all players have left the game. The challenge arises for datasets with incomplete churning information for all players, as most of them still connect to the game. This is called a censored data problem and is in the nature of churn. Censoring is commonly dealt with survival analysis techniques, but due to the inflexibility of the survival statistical algorithms, the accuracy achieved is often poor. In contrast, novel ensemble learning techniques, increasingly popular in a variety of scientific fields, provide high-class prediction results. In this work, we develop, for the first time in the social games domain, a survival ensemble model which provides a comprehensive analysis together with an accurate prediction of churn. For each player, we predict the probability of churning as function of time, which permits to distinguish various levels of loyalty profiles. Additionally, we assess the risk factors that explain the predicted player survival times. Our results show that churn prediction by survival ensembles significantly improves the accuracy and robustness of traditional analyses, like Cox regression.
In Nigeria, unemployment and poverty are forcing many young people to turn to crime and they are tapping into the country's rich resources of oil. Africa's largest producer of crude oil is losing millions of dollars because of theft, but some locals say the illicit way is their only means of survival.