Modeling medical prognosis: survival analysis techniques.
Medical prognosis has played an increasing role in health care. Reliable prognostic models that are based on survival analysis techniques have been recently applied to a variety of domains, with varying degrees of success. In this article, we review some methods commonly used to model time-oriented data, such as Kaplan-Meier curves, Cox proportional hazards, and logistic regression, and discuss their applications in medical prognosis. Nonlinear, nonparametric models such as neural networks have increasingly been used for building prognostic models. We review their use in several medical domains and discuss different implementation strategies. Advantages and disadvantages of these methods are outlined, as well as pointers to pertinent literature.
Ohno-Machado
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Decision Systems Group, Brigham and Women's Hospital, Health Science and Technology Division, Harvard Medical School, Massachusetts Institute of Technology, 75 Francis Street, Boston, Massachusetts 02115, USA. machado@dsg.harvard.edu
D006801:Humans; D015993:Life Tables; D016015:Logistic Models; D008490:Medical Informatics; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011379:Prognosis; D016016:Proportional Hazards Models; D016019:Survival Analysis