Model selection for a medical diagnostic decision support system: A breast cancer detection case

Artificial Intelligence in Medicine

Volumn 20, Issue 3, 2000, Pages 183-204

  West, David ^a   West, Vivian ^a,b  

^a EAST CAROLINA UNIVERSITY   (United States)

^b UNIVERSITY OF NORTH CAROLINA   (United States)

Author keywords

Decision support system; Model selection; Neural network; Self organizing map; Stacked generalization

Indexed keywords

DATABASE SYSTEMS; DECISION SUPPORT SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; ONCOLOGY; PHYSIOLOGICAL MODELS;

MEDICAL DIAGNOSTIC DECISION SUPPORT SYSTEMS (MDSS); SELF-ORGANIZING MAPS (SOM);

COMPUTER AIDED DIAGNOSIS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CANCER DIAGNOSIS; CYTOLOGY; DATA BASE; DECISION MAKING; HUMAN; INFORMATION SYSTEM; MODEL; PRIORITY JOURNAL; REGRESSION ANALYSIS; STATISTICAL MODEL; TECHNIQUE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; DATA DISPLAY; DATABASES, FACTUAL; DECISION SUPPORT SYSTEMS, CLINICAL; DIAGNOSTIC ERRORS; HUMANS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER);

EID: 0034333656     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0933-3657(00)00063-4     Document Type: Article

References (58)

1. Baker J.A., Kornguth P.J., Lo J.Y., Williford M.E., Floyd C.E. Breast cancer: prediction with artificial neural network based on bi-rads standardized lexicon. Radiology. 196:1995;817-822.
2. Baker J.A., Kornguth P.J., Lo J.Y., Floyd C.E. Artificial neural network: improving the quality of breast biopsy recommendations. Radiology. 198:1996;131-135.
3. Baxt W.G. Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute coronary occlusion. Neural Comput. 2:1990;480-489.
4. Baxt W.G. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Internal Med. 115:1991;843-848.
5. Baxt W.G. A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching. Med. Decis. Mak. 14:1994;217-222.
6. Becker R.L. Computer-assisted image classification: use of neural networks in anatomic pathology. Cancer Lett. 77:1994;111-117.
7. Bottaci L., Drew P.J., Hartley J.E., Hadfieldet M.B. Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet. 350:1997;469-472.
8. Bounds D.G., Lloyd P.J., Mathew B.G. A comparison of neural network and other pattern recognition approaches to the diagnosis of low back disorders. Neural Networks. 3:1990;583-591.
9. Breiman L. Stacked regressions. Machine Learning. 24:1995;49-64.
10. Breiman L. Bagging predictors. Machine Learning. 26:1996;123-140.
11. Centor R.M. Signal detectability: the use of ROC curves and their analyses. Med. Decis. Mak. 11:1991;102-106.
12. Chiou Y.S.P., Lure Y.M.F. Hybrid lung nodule detection (HLND) system. Cancer Lett. 77:1994;119-126.
13. Detrano R., Janosi A., Steinbrun W., Pfisterer M., Schmid J., Sandhu S., Guppy K.H., Lee S., Froelicher V. International application of a new probability algorithm for the diagnosis of coronary artery disease. Am. J. Cardiol. 64:1989;304-310.
14. Dougados M., van der Linden S., Juhlin R., Huitfeldt B., Amor B., Calin A., Cats A., Dijkmans B., Olivieri I., Pasero G., Veys E., Zeidler H. The European spondylarthropathy study group preliminary criteria for the classification of spondylarthropathy. Arthritis Rheum. 34(10):1991;1218-1230.
15. Fricker J. Artificial neural networks improve diagnosis of acute myocardial infarction. Lancet. 350:1997;935.
16. Gaetz M., Weinberg H., Rzempoluck E., Jantzen K.J. Neural network classification and correlation analysis of EEG and MEG activity accompanying spontaneous reversals of the Necker cube. Cogn. Brain Res. 6:1998;335-346.
17. Gilpin E., Olshen R., Henning H., Ross J. Jr. Risk prediction after myocardial infarction. Cardiology. 70:1983;73-84.
18. Goldman L, Weinberg M, Weisberg M, Olshen R, Cook EF, Sargent RK, Lamas GA, Dennis C, Wilson C, Deckelbaum L, Fineberg H, Striatelli R, the medical staff at Yale-New Haven Hospital and Brigham and Women's Hospital. A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain. N Engl J Med 1982;307(10):588-96.
19. Hand DJ. Construction and assessment of classification rules. New York: Wiley, 1997.
20. Haykin S. Neural Networks: A Comprehensive Foundation. New York: Macmillan, 1994.
21. Henson D.B., Spenceley S.E., Bull D.R. Artificial neural network analysis of noisy visual field data in glaucoma. Artif. Intell. Med. 10:1997;99-113.
22. Hilden J. The area under the ROC curve and its competitors. Med. Decis. Mak. 11:1991;95-101.
23. Hubbard B.L., Gibbons R.J., Lapeyre A.C., Zinsmeister A.R., Clements I.P. Identification of severe coronary artery disease using simple clinical parameters. Arch. Internal Med. 152:1992;309-312.
24. Jacobs R.A., Jordan M.I., Hinton S.J., Hinton G.E. Adaptive mixtures of local experts. Neural Comput. 3:1991;79-87.
25. Jiang Y., Nishikawa R.M., Wolverton D.E., Metz C.E., Giger M.L., Schmidt R.A., Vyborny C.J., Doi K. Malignant and benign clustered microcalcification: automated feature analysis and classification. Radiology. 198:1996;671-678.
26. Josefson D. Computers beat doctors in interpreting ECGs. Br. Med. J. 315:1997;764-765.
27. Kohonen T. Self-organizing maps. New York: Springer, 1997.
28. Lapuerta P., L'italien G.J., Paul S., Hendel R.C., Leppo J.A., Fleisher L.A., Cohen M.C., Eagle K.A., Giugliano R.P. Neural network assessment of perioperative cardiac risk in vascular surgery patients. Med. Decis. Mak. 18(1):1998;70-75.
29. Lim C.P., Harrison R.F., Kennedy R.L. Application of autonomous neural network systems to medical pattern classification tasks. Artif. Intell. Med. 11:1997;215-239.
30. Linkensand D.A., Vefghi L. Recognition of patient anesthetic levels: neural network systems, principal component analysis, and canonical discriminant variates. Artif. Intell. Med. 11:1997;155-173.
31. Maclin P.S., Dempsey J. How to improve a neural network for early detection of hepatic cancer. Cancer Lett. 77:1994;95-101.
32. Makuch R.W., Rosenberg P.S. Identifying prognostic factors in binary outcome data: an application using liver function tests and age to predict liver metastases. Statist. Med. 7:1988;843-856.
33. Mangasarian O.L., Street W.N., Wolberg W.H. Breast cancer diagnosis and prognosis via linear programming. Operations Res. 43(4):1995;570-577.
34. Mango L.J. Computer-assisted cervical cancer screening using neural networks. Cancer Lett. 77:1994;155-162.
35. Mango L.J. Reducing false negatives in clinical practice: the role of neural network technology. Am. J. Obstet. Gynecol. 175(4):1996;1114-1119.
36. Marble R.P., Healy J.C. A neural network approach to the diagnosis of morbidity outcomes in trauma care. Artif. Intell. Med. 15:1999;299-307.
37. Miller R.A. Medical diagnostic decision support systemspast, present, and future: a threaded bibliography and brief commentary. J. Am. Med. Informatics Assoc. 1(1):1994;8-27.
38. Naguib R.N.G., Hamdy F.C. A general regression neural network analysis of prognostic markers in prostate cancer. Neurocomputing. 19:1998;145-150.
39. Nomura H., Kashiwagi S., Hayashi J., Kajiyama W., Ikematsu H., Noguchi A., Tani S., Goto M. Prevalence of gallstone disease in a general population of Okinawa, Japan. Am. J. Epidemiol. 128(3):1988;598-605.
40. Rosenberg C., Erel J., Atlan H. A neural network that learns to interpret myocardial planar thallium scintigrams. Neural Comput. 5:1993;492-502.
41. Salchenberger L., Venta E.R., Venta L.A. Using neural networks to aid the diagnosis of breast implant rupture. Comput. Operations Res. 24(5):1997;435-444.
42. Schubert T.T., Bologna S.D., Nensey Y., Schubert A., Mascha E.J., Ma C.K. Ulcer risk factors: interactions between heliocobacter pylori infection, nonsteroidal use and age. Am. J. Med. 94:1993;416-418.
43. Sheppard D., McPhee D., Darke C., Shrethra B., Moore R., Jurewits A., Gray A. Predicting cytomegalovirus disease after renal transplantation: an artificial neural network approach. Int. J. Med. Informatics. 54(1):1999;55-71.
44. Somoza E., Somoza J.R. A neural network approach to predicting admission decisions in a psychiatric emergency room. Med. Decis. Mak. 13:1993;273-280.
45. Specht D.F. A general regression neural network. Neural Networks. 2(6):1991;568-576.
46. Tierney W.H., Murray M.D., Gaskins D.L., Zhou X.-H. Using computer-based medical records to predict mortality risk for inner-city patients with reactive airways disease. J. Med. Informatics Assoc. 4:1997;313-321.
47. Titterington D.M., Murray G.D., Murray L.S., Spiegelhalter D.J., Skene A.M., Habbema J.D.F., Gelpke G.J. Comparison of discrimination techniques applied to a complex data set of head injured patients. J. R. Statist. Soc. A. 144(2):1981;145-175.
48. Tourassi G.D., Floyd C.E., Sostman H.D., Coleman R.E. Acute pulmonary embolism: artificial neural network approach for diagnosis. Radiology. 189:1993;555-558.
49. Tsujji O., Freedman M.T., Mun S.K. Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network. Pattern Recogn. 32:1999;891-903.
50. Tu JV, Weinstein MC, McNeil BJ, Naylor CD, The Steering Committee of the Cardiac Care Network of Ontario. Predicting mortality after coronary artery bypass surgery: what do artificial neural networks learn? Med Decis Mak 1998;18(2):229-35.
51. Wilding P., Morgan M.A., Grygotis A.E., Shoffner M.A., Rosato E.F. Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. Cancer Lett. 77:1994;145-153.
52. Wolberg W.H., Street W.N., Mangasarian O.L. Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Anal. Quant. Cytol. Histol. 17(2):1995;77-87.
53. Wolberg W.H., Street W.N., Heisey D.M., Mangasarian O.L. Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Arch. Surgery. 130:1995;511-516.
54. Wolpert D. Stacked generalization. Neural Networks. 5:1992;241-259.
55. Wu Y., Giger M.L., Doi K., Vyborny C.J., Schmidt R.A., Metz C.E. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology. 187:1993;81-87.
56. Zhang J. Developing robust non-linear models through bootstrap aggregated neural networks. Neurocomputing. 25:1999;93-113.
57. Zhang J. Inferential estimation of polymer quality using bootstrap aggregated neural networks. Neural Networks. 12:1999;927-938.
58. Zhou J., Lopresti D. Improving classifier performance through repeated sampling. Pattern Recogn. 30(1):1997;1637-1650.