107 related articles for article (PubMed ID: 18533119)
1. Prediction of pelvic organ prolapse using an artificial neural network.
Robinson CJ; Swift S; Johnson DD; Almeida JS
Am J Obstet Gynecol; 2008 Aug; 199(2):193.e1-6. PubMed ID: 18533119
[TBL] [Abstract][Full Text] [Related]
2. Application of artificial neural networks to establish a predictive mortality risk model in children admitted to a paediatric intensive care unit.
Chan CH; Chan EY; Ng DK; Chow PY; Kwok KL
Singapore Med J; 2006 Nov; 47(11):928-34. PubMed ID: 17075658
[TBL] [Abstract][Full Text] [Related]
3. Can we screen for pelvic organ prolapse without a physical examination in epidemiologic studies?
Barber MD; Neubauer NL; Klein-Olarte V
Am J Obstet Gynecol; 2006 Oct; 195(4):942-8. PubMed ID: 16681989
[TBL] [Abstract][Full Text] [Related]
4. Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks.
Nilsson J; Ohlsson M; Thulin L; Höglund P; Nashef SA; Brandt J
J Thorac Cardiovasc Surg; 2006 Jul; 132(1):12-9. PubMed ID: 16798296
[TBL] [Abstract][Full Text] [Related]
5. Prediction of target range of intact parathyroid hormone in hemodialysis patients with artificial neural network.
Wang YF; Hu TM; Wu CC; Yu FC; Fu CM; Lin SH; Huang WH; Chiu JS
Comput Methods Programs Biomed; 2006 Aug; 83(2):111-9. PubMed ID: 16839639
[TBL] [Abstract][Full Text] [Related]
6. Combination of multiple serum markers using an artificial neural network to improve specificity in discriminating malignant from benign pelvic masses.
Zhang Z; Barnhill SD; Zhang H; Xu F; Yu Y; Jacobs I; Woolas RP; Berchuck A; Madyastha KR; Bast RC
Gynecol Oncol; 1999 Apr; 73(1):56-61. PubMed ID: 10094881
[TBL] [Abstract][Full Text] [Related]
7. Assessment of individual risk of death using self-report data: an artificial neural network compared with a frailty index.
Song X; Mitnitski A; MacKnight C; Rockwood K
J Am Geriatr Soc; 2004 Jul; 52(7):1180-4. PubMed ID: 15209659
[TBL] [Abstract][Full Text] [Related]
8. Applying an artificial neural network to predict osteoporosis in the elderly.
Chiu JS; Li YC; Yu FC; Wang YF
Stud Health Technol Inform; 2006; 124():609-14. PubMed ID: 17108584
[TBL] [Abstract][Full Text] [Related]
9. A decision support system to facilitate management of patients with acute gastrointestinal bleeding.
Chu A; Ahn H; Halwan B; Kalmin B; Artifon EL; Barkun A; Lagoudakis MG; Kumar A
Artif Intell Med; 2008 Mar; 42(3):247-59. PubMed ID: 18063351
[TBL] [Abstract][Full Text] [Related]
10. Prediction of radiation induced liver disease using artificial neural networks.
Zhu J; Zhu XD; Liang SX; Xu ZY; Zhao JD; Huang QF; Wang AY; Chen L; Fu XL; Jiang GL
Jpn J Clin Oncol; 2006 Dec; 36(12):783-8. PubMed ID: 17068085
[TBL] [Abstract][Full Text] [Related]
11. Artificial neural network prediction of clozapine response with combined pharmacogenetic and clinical data.
Lin CC; Wang YC; Chen JY; Liou YJ; Bai YM; Lai IC; Chen TT; Chiu HW; Li YC
Comput Methods Programs Biomed; 2008 Aug; 91(2):91-9. PubMed ID: 18508152
[TBL] [Abstract][Full Text] [Related]
12. Matrix metalloproteinases-1, -2 expression in uterosacral ligaments from women with pelvic organ prolapse.
Strinic T; Vulic M; Tomic S; Capkun V; Stipic I; Alujevic I
Maturitas; 2009 Oct; 64(2):132-5. PubMed ID: 19765922
[TBL] [Abstract][Full Text] [Related]
13. Predicting hypotensive episodes during spinal anesthesia with the application of artificial neural networks.
Lin CS; Chiu JS; Hsieh MH; Mok MS; Li YC; Chiu HW
Comput Methods Programs Biomed; 2008 Nov; 92(2):193-7. PubMed ID: 18760495
[TBL] [Abstract][Full Text] [Related]
14. Assessment of fall-risk by means of a neural network based on parameters assessed by a wearable device during posturography.
Giansanti D; Maccioni G; Cesinaro S; Benvenuti F; Macellari V
Med Eng Phys; 2008 Apr; 30(3):367-72. PubMed ID: 17560825
[TBL] [Abstract][Full Text] [Related]
15. Does pelvic organ prolapse reduction affect abdominal leak point pressures?
Whiteside JL; Viazmenski A; Strohbehn K; Hanissian PD
J Reprod Med; 2008 Apr; 53(4):294-8. PubMed ID: 18472654
[TBL] [Abstract][Full Text] [Related]
16. Generation of classification criteria for chronic fatigue syndrome using an artificial neural network and traditional criteria set.
Linder R; Dinser R; Wagner M; Krueger GR; Hoffmann A
In Vivo; 2002; 16(1):37-43. PubMed ID: 11980359
[TBL] [Abstract][Full Text] [Related]
17. Artificial neural networks based prediction of cerebral palsy in infants with central coordination disturbance.
Lukić S; Ćojbašić Ž; Jović N; Popović M; Bjelaković B; Dimitrijević L; Bjelaković L
Early Hum Dev; 2012 Jul; 88(7):547-53. PubMed ID: 22281057
[TBL] [Abstract][Full Text] [Related]
18. [The application of artificial neural networks to predict individual risk of essential hypertension].
Zhou SH; Nie SF; Wang CJ; Wei S; Xu YH; Li XH; Song EM
Zhonghua Liu Xing Bing Xue Za Zhi; 2008 Jun; 29(6):614-7. PubMed ID: 19040050
[TBL] [Abstract][Full Text] [Related]
19. A comparison of three computational modelling methods for the prediction of virological response to combination HIV therapy.
Wang D; Larder B; Revell A; Montaner J; Harrigan R; De Wolf F; Lange J; Wegner S; Ruiz L; Pérez-Elías MJ; Emery S; Gatell J; D'Arminio Monforte A; Torti C; Zazzi M; Lane C
Artif Intell Med; 2009 Sep; 47(1):63-74. PubMed ID: 19524413
[TBL] [Abstract][Full Text] [Related]
20. [Artificial neural network in the prediction of nosocomial infection risk].
Xu LY; Bai Y; Hu M; Xu YY; Sun ZQ
Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2006 Jun; 31(3):404-7. PubMed ID: 16859134
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
