Methodological approach to the use of artificial neural networks for predicting results in medicine;Aproximación metodológica al uso de redes neuronales artificiales para la predicción de resultados en medicina

Medicina Clinica

Volumn 122, Issue SUPPL. 1, 2004, Pages 59-67

  Trujillano, Javier ^a   March, Jaume ^a   Sorribas, Albert ^a  

^a UNIVERSITY OF LLEIDA   (Spain)

Author keywords

Artificial neural networks; Logistic regression; Outcome prediction

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOMEDICINE; CLINICAL PRACTICE; INTERMETHOD COMPARISON; LOGISTIC REGRESSION ANALYSIS; METHODOLOGY; MULTILAYER PERCEPTION TRAINED WITH BACK PROPAGATION ALGORITHM; NONLINEAR SYSTEM; OUTCOMES RESEARCH; PREDICTION; PROBABILITY; REVIEW; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; HUMANS; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER); OUTCOME ASSESSMENT (HEALTH CARE); PREDICTIVE VALUE OF TESTS; RISK ASSESSMENT;

EID: 1642359158     PISSN: 00257753     EISSN: None     Source Type: Journal    
DOI: 10.1157/13057536     Document Type: Review

References (52)

1. Steyerberg EW, Eijkemans MJC, Harrell FE, Habbema JDF. Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Statist Med 2000;19:1059-79.
2. Burke HB. Artificial neural networks for cancer research: outcome prediction. Sem in Surg Oncol 1994;10:73-9.
3. Cross BS, Harrison RF, Kennedy RL. Introduction to neural networks. Lancet 1995;346:1075-9.
4. Sarle WS. Neural network FAQ, part 1 of 7: introduction, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/ pub/neural/FAQ.html, 1997.
5. Bishop CM. Neural networks for pattern recognition. Oxford: Oxford University, 1995.
6. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995;346:1135-8.
7. Armoni A. Use of neural networks in medical diagnosis. MD Computing 1998;15:100-4.
8. Dybowski R, Gant V. Artificial neural networks in pathology and medical laboratories. Lancet 1995;346:1203-7.
9. Orr RK. Use of a probabilistic neural network to estimate the risk of mortality after cardiac surgery. Med Decis Making 1997;17:178-85.
10. Dorsey SG, Waltz CF, Brosch L, Connerney I, Schweitzer EJ, Barlett ST. A neural network model for predicting pancreas transplant graft outcome. Diabetes Care 1997;20:1128-33.
11. Lapuerta P, Rajan S, Bonacini M. Neural networks as predictors of outcomes in alcoholic patients with severe liver disease. Hepatology 1997;25:302-6.
12. Wong LSS, Young JD. A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural network. Anaesthesia 1999;54:1048-54.
13. Snow PB, Kerr DJ, Brandt JM, Rodvold DM. Neural network and regression predictions of 5-year survival after colon carcinoma treatment. Cancer 2001;91:1673-8.
14. Montie JE, Wei JT. Artificial neural networks for prostate carcinoma risk assessment. An overview. Cancer 2001;91:1647-52.
15. Ellenius J, Groth T, Lindahl B, Wallentin L. Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis. Clin Chem 1997;43:1919-25.
16. Nakamura K, Yoshida H, Engelmann R, MacMahon H, Katsuragawa S, Ishida T, et al. Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks. Radiology 2000;214:823-30.
17. Eberhart RC, Dobbins RW, Hutton LV. Neural network paradigm comparisons for appendicitis diagnosis. En: Proceedings of the fourth annual IEEE Symposium on Computer-based Medical Systems. Los Alamitos, CA: IEEE Computer Society Press, 1991; p. 298-304.
18. El-Solh AA, Hisao C, Goodnough S, Serghani J, Grant BJB. Predicting active pulmonary tuberculosis using an artificial neural network. Chest 1999;116:968-73.
19. Somoza E, Somoza JR. A neural-network approach to predicting admission decisions in a psychiatric emergency room. Med Decis Making 1993;13:273-80.
20. Hare BJ, Prestegard JH. Application of neural networks to automated assigment of NMR spectra of proteins J Biomol NMR 1994;4:35-46.
21. Kippenhan JS, Barker WW, Angel J, Grady C, Duara R. Neural-networks clasiffication of normal and Alzheimer's disease subjects using high-resolution and low-resolution PET cameras. J Nucl Med 1994;35:7-15.
22. Edenbrandt L, Devine B, Macfarlane PW. Classification of electrocardiographic ST-T segments-human expert vs artificial neural network. Eur Heart J 1993;14:464-8.
23. Anderer P, Saleteu B, Kloppel B, Semlitsch HV, Werner H. Discrimination between demented patients and normal based on topographic EEG slow wave activity: comparison between z statistics, discriminant analysis and artificial neural network classifiers. Electroencephalogr Clin Neuropsychol 1994;91:108-17.
24. Yamamura S, Nishizawa K, Hirano M, Momose Y, Kimura A. Prediction of plasma levels of aminoglycoside antibiotic in patients with severe illness by means of an artificial neural network simulator. J Pharm Sci 1998;1:95-101.
25. Dayhoff JE, DeLeo JM. Artificial neural networks. Opening the black box. Cancer 2001;91:1615-35.
26. Martín del Brio B, Sanz Molina A. Redes neuronales y sistemas borrosos. Madrid: RA-MA, 1997.
27. Eldar S, Siegelmann HT, Buzaglo D, Matter I, Cohen A, Sabo E, et al. Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute cholecystitis: artificial neural networks improve the prediction of conversion. World J Surg 2002;26:79-85.
28. Duh MS, Walker AM, Pagano M, Kronlund K. Prediction and cross-validation of neural networks versus logistic regression: using hepatic disorders as an example. Am J Epidemiol 1998;147:407-13.
29. Borque A, Sanz G, Allepuz C, Plaza L, Rioja LA. The use of neural networks and logistic regression analysis for predicting pathological stage in men undergoing radical prostatectomy: a population based study. J Urol 2001;166:1672-8.
30. Sinha M, Kennedy CS, Ramundo ML. Artificial neural network predicts CT scan abnormalities in paediatric patients with closed head injury. J Trauma 2001;50:308-12.
31. Sarle WS. Neural networks and statistical models. Proceedings of the 19th annual SAS Users Group International Conference, Cary, NC (SAS Institute), 1994;1538-50.
32. Tu JV, Guerriere MRJ. Use of a neural network as a predictive instrument for length of stay in the intensive care unit following cardiac surgery. Comput Biomed Res 1993;26:220-9.
33. Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996;49;11:1225-31.
34. Sargent DJ. Comparison of artificial neural networks with other statistical approaches. Results from medical data sets. Cancer 2001;91:1636-42.
35. Lang EW, Pitts LH, Damron SL, Rutledge R. Outcome after severe head injury: an analysis of prediction based upon comparison of neural network versus logistic regression analysis. Neurol Res 1997;19:274-80.
36. DiRusso SM, Sullivan T, Holly C, Nealon S, Savino J. An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area. J Trauma 2000;49:212-23.
37. Selker HP, Griffith JL, Patil S, Long WJ, D'Agostino RB. A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. J Investig Med 1995;43:468-76.
38. Schwarzer G, Vach W, Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Statist Med 2000;19:541-61.
39. Levine RF. Conference concluding remarks. Cancer 2001;91:1696-7.
40. Lippmann RP, Shahian DM. Coronary artery bypass risk prediction using neural networks. Ann Thorac Surg 1997;63:1635-43.
41. Guh JY, Yang CY, Yang JM, Chen LM, Lai YH. Prediction of equilibrated postdialysis BUN by an artificial neural network in high-efficiency hemodialysis. Am J Kidney Dis 1998;31:638-46.
42. Clermont G, Angus DC, DiRusso SM, Griffin M, Linde-Zwirble WT. Predicting hospital mortality for patients in the intensive care unit: a comparison of artificial neural networks with logistic regression models. Crit Care Med 2001;29:291-6.
43. Tourassi GD, Floyd CE. The effect of data sampling on the performance evaluation of artificial neural networks in medical diagnosis. Med Decis Making 1997;17:186-92.
44. Sarle WS. Neural Network FAQ, part 5 and 6 of 7: free software and commercial software, Disponible en: URL: ftp://ftp.sas.com/pub/neural/FAQ. html, 1997.
45. Doyle HR, Dvorchik I, Mitchell S, Marino JR, Ebert FH, McMichael J, et al. Predicting outcomes after liver transplantation. A connectionist approach. Ann Surg 1994;219:408-15.
46. Concato J. Challenges in prognostic analysis. Cancer 2001;91:1607-14.
47. Van Houwelingen HC. Validation, calibration, revision and combination of prognostic survival models. Statist Med 2000;19:3401-15.
48. Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982;143:29-36.
49. Burgueño MJ, Garcia-Bastos JL, González-Buitrago JM. Las curvas ROC en la evaluación de las pruebas diagnósticas. Med Clin (Barc) 1995;104:661-70.
50. Lemeshow S, Hosmer DW. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol 1982;115:92-106.
51. Jefferson MF, Pendleton N, Lucas SB, Horan MA. Comparison of a genetic algorithm neural network with logistic regression for predicting outcome after surgery for patients with non small cell lung carcinoma. Cancer 1997;79:1338-42.
52. Dybowski R, Weller P, Chang R, Gant V. Prediction of outcome in critically ill patients using artificial neural network synthesised by genetic algorithm. Lancet 1996;347:1146-50.