Conditional Hazard Estimating Neural Networks

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Author(s): Antonio Eleuteri (Royal Liverpool University Hospital, UK), Azzam Taktak (Royal Liverpool University Hospital, UK), Bertil Damato (Royal Liverpool University Hospital, UK), Angela Douglas (Liverpool Women’s Hospital, UK)and Sarah Coupland (Royal Liverpool University Hospital, UK)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Artificial Intelligence
Source Author(s)/Editor(s): Juan Ramón Rabuñal Dopico (University of A Coruña, Spain), Julian Dorado (University of A Coruña, Spain)and Alejandro Pazos (University of A Coruña, Spain)
DOI: 10.4018/978-1-59904-849-9.ch060

Keywords: Artificial Intelligence / Computer Science & IT / Information Science Reference

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Abstract

Survival analysis is used when we wish to study the occurrence of some event in a population of subjects and the time until the event of interest. This time is called survival time or failure time. Survival analysis is often used in industrial life-testing experiments and in clinical follow-up studies. Examples of application include: time until failure of a light bulb, time until occurrence of an anomaly in an electronic circuit, time until relapse of cancer, time until pregnancy. In the literature we find many different modeling approaches to survival analysis. Conventional parametric models may involve too strict assumptions on the distributions of failure times and on the form of the influence of the system features on the survival time, assumptions which usually extremely simplify the experimental evidence, particularly in the case of medical data (Cox & Oakes, 1984). In contrast, semiparametric models do not make assumptions on the distributions of failures, but instead make assumptions on how the system features influence the survival time (the usual assumption is the proportionality of hazards); furthermore, these models do not usually allow for direct estimation of survival times. Finally, non-parametric models usually only allow for a qualitative description of the data on the population level. Neural networks have recently been used for survival analysis; for a survey on the current use of neural networks, and some previous attempts at neural network survival modeling we refer to (Bakker & Heskes, 1999), (Biganzoli et al., 1998), (Eleuteri et al., 2003), (Lisboa et al., 2003), (Neal, 2001), (Ripley & Ripley, 1998), (Schwarzer et al. 2000). Neural networks provide efficient parametric estimates of survival functions, and, in principle, the capability to give personalised survival predictions. In a medical context, such information is valuable both to clinicians and patients. It helps clinicians to choose appropriate treatment and plan follow-up efficiently. Patients at high risk could be followed up more frequently than those at lower risk in order to channel valuable resources to those who need them most. For patients, obtaining information about their prognosis is also extremely valuable in terms of planning their lives and providing care for their dependents. In this article we describe a novel neural network model aimed at solving the survival analysis problem in a continuous time setting; we provide details about the Bayesian approach to modeling, and a sample application on real data is shown.

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