On the limited memory BFGS method for large scale optimization
Mathematical Programming: Series A and B
Regression Modeling Strategies
Regression Modeling Strategies
Machine learning for survival analysis: a case study on recurrence of prostate cancer
Artificial Intelligence in Medicine
On the use of artificial neural networks for the analysis of survival data
IEEE Transactions on Neural Networks
A machine learning approach for identifying subtypes of autism
Proceedings of the 1st ACM International Health Informatics Symposium
Live logic™: method for approximate knowledge discovery and decision making
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
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In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordance index (CI) is a typical metric for quantifying the predictive ability of a survival model. We propose a new algorithm that directly uses the CI as the objective function to train a model, which predicts whether an event will eventually occur or not. Directly optimizing the CI allows the model to make complete use of the information from both censored and non-censored observations. In particular, we approximate the CI via a differentiable function so that gradient-based methods can be used to train the model. We applied the new algorithm to predict the eventual recurrence of prostate cancer following radical prostatectomy. Compared with the traditional Cox proportional hazards model and several other algorithms based on neural networks and support vector machines, our algorithm achieves a significant improvement in being able to identify high-risk and low-risk groups of patients.