A novel approach for accurate prediction of spontaneous passage of ureteral stones: Support vector machines

Kidney International

Volumn 69, Issue 1, 2006, Pages 157-160

  Dal Moro, F ^a,c   Abate, A ^b   Lanckriet, G R G ^b   Arandjelovic, G ^a   Gasparella, P ^a   Bassi, P ^a   Mancini, M ^a   Pagano, F ^a  

^a UNIVERSITY OF PADOVA   (Italy)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF PADUA MEDICAL SCHOOL   (Italy)

Author keywords

Artificial intelligence; Neural networks; Statistical methods; Support vector machine; Ureteral calculi; Urolithiasis

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER PREDICTION; COMPUTER PROGRAM; DIAGNOSTIC ACCURACY; DISEASE DURATION; FISHER EXACT TEST; KIDNEY COLIC; LOGISTIC REGRESSION ANALYSIS; PRIORITY JOURNAL; RANK SUM TEST; SCORING SYSTEM; URETER STONE; UROLITHIASIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER); URETERAL CALCULI;

EID: 30944439863     PISSN: 00852538     EISSN: 15231755     Source Type: Journal    
DOI: 10.1038/sj.ki.5000010     Document Type: Article

References (18)

1. Segura JW, Preminger GM, Assimos DG et al. Ureteral stones clinical guidelines panel summary report on the management of ureteral calculi. J Urol 1997; 158: 1915-1921.
2. Anagnostu T, Tolley D. Management of ureteric stones. Eur Urol 2004; 45: 714-721.
3. Miller OF, Kane CJ. Time to stone passage for observed ureteral calculi: a guide for patient education. J Urol 1999; 162: 688-690.
4. Parekattil SJ, White MD, Moran ME, Kogan BA. A computer model to predict the outcome and duration of ureteral or renal calculous passage. J Urol 2004; 171: 1436-1439.
5. Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl 2004; 86: 334-338.
6. Cristianini N, Schoelkopf B. Support vector machines and kernel methods. Al Mag 2002; 23: 31-41.
7. Boser BE, Guyon I, Vapnik V. A Training algorithm for optimal margin classifiers. Proc Comput Learn Theory 1992: 144-152, ACM Press.
8. Cristianini N, Shawe-Taylor J. An Introduction to Support Vector Machines. Cambridge University Press: Cambridge; 2000.
9. Bradley PS, Mangasarian OL, Street WN. Feature selection via mathematical programming. INFORMS J Comput 1998; 10: 209-217.
10. Tu JV. Advantages and disadvantages of using artificial neural networks vs logistic regression for predicting medical outcomes. J Clin Epidemiol 1996; 49: 1225-1231.
11. Batuello JT, Gamito EJ, Crawford E et al. Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer. Urology 2001; 57: 481-485.
12. Cummings JM, Boullier JA, Izenberg SD et al. Prediction of spontaneous ureteral calculous passage by an artificial neural network. J Urol 2000; 164: 326-328.
13. Bagli DJ, Agarwal SK, Venkateswaran S et al. Artificial neural networks in pediatric urology: prediction of sonographic outcome following pyeloplasty. J Urol 1998; 160: 980-983.
14. Russel S, Norvig P. Artificial Intelligence, A Modern Approach. 2nd edn, Prentice-Hall: Englewood Cliffs, NJ.
15. Leane MM, Cumming I, Corrigan OI. The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets. PharmSciTech 2003; 4: E26.
16. Porpiglia F, Ghignone G, Fiori C et al. Nifedipine versus Tamsulosin for the management of lower ureteral stones. J Urol 2004; 172: 568-571.
17. Dellabella M, Milanese G, Muzzonigro G. Efficacy of Tamsulosin in the medical management of juxtavesical ureteral stones. J Urol 2003; 170: 2202-2205.
18. Gomha MA, Sheir KZ, Showky S et al. Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model? J Urol 2004; 172: 175-179.