title: Censored lifetime data in adaptive neural networks.
abstract: OBJECTIVE
  In clinical research, survival, reliability and failure analyses, the use of censored lifetime data often becomes a necessity. In this paper we present a novel methodology developed to allow for the use of censored data to train neural networks to predict the time of specific adverse events.
  
  
  RESULTS
  Specifically, for patients with implanted bioprostheses, we were able to design and train a neural system to successfully predict the time from valve implant to valve dysfunction. Further, we were able to demonstrate the clear improvement in performance and predictive accuracy of the system when trained using this method. The assertion that censored data carry additional and extremely valuable information, especially in cases of rare events, is substantiated by this correlation analysis.
  
  
  CONCLUSIONS
  This new methodology, in combination with results obtained from previous models which were able to identify the patients most likely to experience such events, now completes the picture by pin-pointing the 'who', as well as the 'when'.
authors.affiliation: CardioVascular Group, Baxter Healthcare Corporation, Irvine, CA 92714, USA.
authors.forename: A S
authors.initials: AS
authors.lastname: Katz
authors.forename: S
authors.initials: S
authors.lastname: Katz
authors.forename: E A
authors.initials: EA
authors.lastname: van der Wal
mesh_terms: D001705:Bioprosthesis / Q000706:statistics & numerical data*; D003625:Data Collection; D005500:Follow-Up Studies; D006349:Heart Valve Diseases / Q000453:epidemiology / Q000601:surgery*; D006350:Heart Valve Prosthesis / Q000706:statistics & numerical data*; D006801:Humans; D016571:Neural Networks, Computer; D011183:Postoperative Complications / Q000453:epidemiology / Q000209:etiology; D011474:Prosthesis Design; D011475:Prosthesis Failure; D012306:Risk; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome