Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains “censored” data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing “omics” data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.
Bankruptcy ; Genomics ; Machine Learning ; Methods ; Survival Analysis

Since the hospital bankruptcy rate is increasing, it has been an important issue to predict the bankruptcy of hospital using the existing hospital management information. Fortunately, the implementation of data mining methodology and decision support system(DSS) are becoming popular. Therefore, this study developed the statistical software for predicting hospital bankruptcy using data mining tool. Stepwise procedures were taken as follows: 1) adopting the HGLM and Logit Models; 2) implementing the input and output processes; 3) linking to the iBITs interface, the data miming tool; and 4) evaluating the software by fitting the hospital management data in practice. The software is written in Visual C++ 5.0 under windows NT/95, and allows the interconnection with other interfaces and libraries. This program initiates encouragement of implementation of DSS models using data mining methodology, in health care fields. This kind of software will play a pivotal role in improving the efficiency and adequacy of managing health care institutions.
Bankruptcy* ; Data Mining* ; Delivery of Health Care ; Logistic Models ; Models, Statistical