title: Time-to-event analysis with artificial neural networks: an integrated analytical and rule-based study for breast cancer.
abstract: This paper presents an analysis of censored survival data for breast cancer specific mortality and disease-free survival. There are three stages to the process, namely time-to-event modelling, risk stratification by predicted outcome and model interpretation using rule extraction. Model selection was carried out using the benchmark linear model, Cox regression but risk staging was derived with Cox regression and with Partial Logistic Regression Artificial Neural Networks regularised with Automatic Relevance Determination (PLANN-ARD). This analysis compares the two approaches showing the benefit of using the neural network framework especially for patients at high risk. The neural network model also has results in a smooth model of the hazard without the need for limiting assumptions of proportionality. The model predictions were verified using out-of-sample testing with the mortality model also compared with two other prognostic models called TNG and the NPI rule model. Further verification was carried out by comparing marginal estimates of the predicted and actual cumulative hazards. It was also observed that doctors seem to treat mortality and disease-free models as equivalent, so a further analysis was performed to observe if this was the case. The analysis was extended with automatic rule generation using Orthogonal Search Rule Extraction (OSRE). This methodology translates analytical risk scores into the language of the clinical domain, enabling direct validation of the operation of the Cox or neural network model. This paper extends the existing OSRE methodology to data sets that include continuous-valued variables.
authors.affiliation: School of Computing and Mathematical Sciences, Liverpool John Moores University, UK. p.j.lisboa@ljmu.ac.uk
authors.forename: Paulo J G
authors.initials: PJ
authors.lastname: Lisboa
authors.forename: Terence A
authors.initials: TA
authors.lastname: Etchells
authors.forename: Ian H
authors.initials: IH
authors.lastname: Jarman
authors.forename: M S
authors.initials: MS
authors.lastname: Hane Aung
authors.forename: Sylvie
authors.initials: S
authors.lastname: Chabaud
authors.forename: Thomas
authors.initials: T
authors.lastname: Bachelot
authors.forename: David
authors.initials: D
authors.lastname: Perol
authors.forename: Thérèse
authors.initials: T
authors.lastname: Gargi
authors.forename: Valérie
authors.initials: V
authors.lastname: Bourdès
authors.forename: Stéphane
authors.initials: S
authors.lastname: Bonnevay
authors.forename: Sylvie
authors.initials: S
authors.lastname: Négrier
mesh_terms: D001943:Breast Neoplasms / Q000401:mortality* / Q000628:therapy*; D015331:Cohort Studies; D018572:Disease-Free Survival; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D009367:Neoplasm Staging; D016571:Neural Networks, Computer; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment; D013997:Time Factors