An integrated framework for risk profiling of breast cancer patients following surgery

Artificial Intelligence in Medicine

Volumn 42, Issue 3, 2008, Pages 165-188

  Jarman, Ian H ^a   Etchells, Terence A ^a   Martín, Jose D ^b   Lisboa, Paulo J G ^a  

^a LIVERPOOL JOHN MOORES UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Artificial neural networks; Breast cancer; Interface; Rule extraction; Survival analysis

Indexed keywords

COMPUTER SIMULATION; MONTE CARLO METHODS; NEURAL NETWORKS; RISK ASSESSMENT; SURGERY;

BREAST CANCER; RULE EXTRACTION; SURVIVAL ANALYSIS;

ONCOLOGY;

ADJUVANT THERAPY; ARTICLE; BREAST CANCER; CANCER CENTER; CANCER PATIENT; CANCER RISK; CANCER SURGERY; CANCER SURVIVAL; CLINICAL FEATURE; CONFIDENCE INTERVAL; DATA BASE; HIGH RISK POPULATION; HUMAN; LONGITUDINAL STUDY; MAJOR CLINICAL STUDY; METHODOLOGY; MONTE CARLO METHOD; NOTTINGHAM EXTENDED ACTIVITIES OF DAILY LIVING SCALE; PRIORITY JOURNAL; PROGNOSIS; RETROSPECTIVE STUDY; UNITED KINGDOM;

ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; CONFIDENCE INTERVALS; DECISION SUPPORT SYSTEMS, CLINICAL; DECISION SUPPORT TECHNIQUES; FEMALE; HEALTH STATUS INDICATORS; HUMANS; INTERNET; MASTECTOMY; MIDDLE AGED; MODELS, BIOLOGICAL; MONTE CARLO METHOD; NEURAL NETWORKS (COMPUTER); PATIENT SELECTION; PROGNOSIS; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES; RISK ASSESSMENT; TREATMENT OUTCOME; USER-COMPUTER INTERFACE;

EID: 39149091349     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2007.11.005     Document Type: Article

References (16)

1. Haybittle J.L., Blamey R.W., Elston C.W., Johnson J., Doyle P.J., Campbell F.C., et al. A prognostic index in primary breast cancer. Br J Cancer 45 (1982) 3621
2. Biganzoli E., Boracchi P., Mariani L., and Marubini E. Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. Stat Med 17 (1998) 1169-1186
3. Lisboa P.J.G., Wong H., Harris P., and Swindell R. A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer. Artif Intell Med 28 1 (2003) 1-25
4. Ravdin P.M., Siminoff L.A., Davis G.J., Mercer B.M., Hewlett J., Gerson N., et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 74 4 (2001) 980-991
5. Cross S.S., Harrison R.F., and Kennedy R.L. Introduction to neural networks. Lancet 346 (1995) 1075-1079
6. Lisboa P.J.G. A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw Invited Paper 15 1 (2002) 9-37
7. Etchells T.A., and Lisboa P.J.G. Orthogonal search-based rule extraction (OSRE) for trained neural networks: a practical and efficient approach. IEEE Trans Neural Netw 17 2 (2006) 374-384
8. Cox D.R. Regression models and life tables. J R Stat Soc B 74 (1972) 187-220
9. Collett D. Modelling survival data in medical research (1994), Chapman and Hall, London
10. Bishop C.M. Neural networks for pattern recognition (1995), Clarendon Press, Oxford
11. Mackay D.J.C. The evidence framework applied to classification networks. Neural Comput 4 5 (1992) 720-736
12. Pop E, Hayward R, Diederich J. RULENEG: extracting rules from a trained ANN by stepwise negation. QUT NRC technical report. Queensland, Australia: Queensland University of Technology, Neurocomputing Research Centre; December 1994.
13. Etchells T.A., Jarman I.H., and Lisboa P.J.G. Empirically derived rules for adjuvant chemotherapy in breast cancer treatment. In: James C.J., Fabri S.G., Camilleri K.P., and Muscat J. (Eds). Proceedings of the IEE medical signal and information processing conference (MEDSIP2004) (September 5-8, 2004), IEE, Malta 345-351
14. Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control 19 (1974) 716-723
15. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Classification and regression trees (1984), Wadsworth International Group, Belmont, CA, USA
16. Kass G.V. An exploratory technique for investigating large quantities of categorical data. Journal of Applied Statistics 29 2 (1980) 119-127