Development of a rule based prognostic tool for HER 2 positive breast cancer patients.
A three stage development process for the production of a hierarchical rule based prognosis tool is described. The application for this tool is specific to breast cancer patients that have a positive expression of the HER 2 gene. The first stage is the development of a Bayesian classification neural network to classify for cancer specific mortality. Secondly, low-order Boolean rules are extracted form this model using an Orthogonal Search based Rule Extraction (OSRE) algorithm. Further to these rules additional information is gathered from the Kaplan-Meier survival estimates of the population, stratified by the categorizations of the input variables. Finally, expert knowledge is used to further simplify the rules and to rank them hierarchically in the form of a decision tree. The resulting decision tree groups all observations into specific categories by clinical profile and by event rate. The practical clinical value of this decision support tool will in future be tested by external validation with additional data from other clinical centres.
Lisboa
Paulo J G
PJ
School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, UK. p.j.lisboa@ljmu.ac.uk
Etchells
Terence A
TA
Jarman
Ian H
IH
Aung
M S Hane
MS
Chabaud
Sylvie
S
Bachelot
Thomas
T
Perol
David
D
Gargi
Thérèse
T
Bourdès
Valérie
V
Bonnevay
Stéphane
S
Négrier
Sylvie
S
D000465:Algorithms; D001943:Breast Neoplasms / Q000378:metabolism* / Q000401:mortality*; D005260:Female; D005602:France / Q000453:epidemiology; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D011379:Prognosis; D016016:Proportional Hazards Models; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism*; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012984:Software; D016019:Survival Analysis; D015996:Survival Rate
D018719:Erb-b2 Receptor Tyrosine Kinases