303 related articles for article (PubMed ID: 25621078)
21. 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]
22. Artificial neural network algorithm model as powerful tool to predict acute lung injury following to severe acute pancreatitis.
Fei Y; Gao K; Li WQ
Pancreatology; 2018 Dec; 18(8):892-899. PubMed ID: 30268673
[TBL] [Abstract][Full Text] [Related]
23. Prognostic factors of peptic ulcer perforation.
Barut I; Tarhan OR; Cerci C; Karaguzel N; Akdeniz Y; Bulbul M
Saudi Med J; 2005 Aug; 26(8):1255-9. PubMed ID: 16127525
[TBL] [Abstract][Full Text] [Related]
24. Use of genetic algorithms for neural networks to predict community-acquired pneumonia.
Heckerling PS; Gerber BS; Tape TG; Wigton RS
Artif Intell Med; 2004 Jan; 30(1):71-84. PubMed ID: 14684266
[TBL] [Abstract][Full Text] [Related]
25. Validation of Boey's score in predicting morbidity and mortality in peptic perforation peritonitis in Northwestern India.
Agarwal A; Jain S; Meena LN; Jain SA; Agarwal L
Trop Gastroenterol; 2015; 36(4):256-60. PubMed ID: 27509704
[TBL] [Abstract][Full Text] [Related]
26. Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data.
Eftekhar B; Mohammad K; Ardebili HE; Ghodsi M; Ketabchi E
BMC Med Inform Decis Mak; 2005 Feb; 5():3. PubMed ID: 15713231
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. Assessment of PULP score in predicting 30-day perforated duodenal ulcer morbidity, and comparison of its performance with Boey and ASA, a retrospective study.
Saafan T; El Ansari W; Al-Yahri O; Eleter A; Eljohary H; Alfkey R; Hajjar M; Toffaha A; El Osta A
Ann Med Surg (Lond); 2019 Jun; 42():23-28. PubMed ID: 31193430
[TBL] [Abstract][Full Text] [Related]
29. Comparison of three data mining models for prediction of advanced schistosomiasis prognosis in the Hubei province.
Li G; Zhou X; Liu J; Chen Y; Zhang H; Chen Y; Liu J; Jiang H; Yang J; Nie S
PLoS Negl Trop Dis; 2018 Feb; 12(2):e0006262. PubMed ID: 29447165
[TBL] [Abstract][Full Text] [Related]
30. Role of Empiric Anti-Fungal Therapy in the Treatment of Perforated Peptic Ulcer Disease: Review of the Evidence and Future Directions.
Huston JM; Kreiner L; Ho VP; Sanders JM; Duane TM
Surg Infect (Larchmt); 2019 Dec; 20(8):593-600. PubMed ID: 31188069
[No Abstract] [Full Text] [Related]
31. Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease.
Cucchetti A; Vivarelli M; Heaton ND; Phillips S; Piscaglia F; Bolondi L; La Barba G; Foxton MR; Rela M; O'Grady J; Pinna AD
Gut; 2007 Feb; 56(2):253-8. PubMed ID: 16809421
[TBL] [Abstract][Full Text] [Related]
32. Forecasting the prognosis of choroidal melanoma with an artificial neural network.
Kaiserman I; Rosner M; Pe'er J
Ophthalmology; 2005 Sep; 112(9):1608. PubMed ID: 16023213
[TBL] [Abstract][Full Text] [Related]
33. Late rectal bleeding after 3D-CRT for prostate cancer: development of a neural-network-based predictive model.
Tomatis S; Rancati T; Fiorino C; Vavassori V; Fellin G; Cagna E; Mauro FA; Girelli G; Monti A; Baccolini M; Naldi G; Bianchi C; Menegotti L; Pasquino M; Stasi M; Valdagni R
Phys Med Biol; 2012 Mar; 57(5):1399-412. PubMed ID: 22349550
[TBL] [Abstract][Full Text] [Related]
34. Preoperative prediction of hepatocellular carcinoma tumour grade and micro-vascular invasion by means of artificial neural network: a pilot study.
Cucchetti A; Piscaglia F; Grigioni AD; Ravaioli M; Cescon M; Zanello M; Grazi GL; Golfieri R; Grigioni WF; Pinna AD
J Hepatol; 2010 Jun; 52(6):880-8. PubMed ID: 20409605
[TBL] [Abstract][Full Text] [Related]
35. Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room.
Jaimes F; Farbiarz J; Alvarez D; Martínez C
Crit Care; 2005 Apr; 9(2):R150-6. PubMed ID: 15774048
[TBL] [Abstract][Full Text] [Related]
36. Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model.
Das A; Ben-Menachem T; Cooper GS; Chak A; Sivak MV; Gonet JA; Wong RC
Lancet; 2003 Oct; 362(9392):1261-6. PubMed ID: 14575969
[TBL] [Abstract][Full Text] [Related]
37. Prediction of bladder outlet obstruction in men with lower urinary tract symptoms using artificial neural networks.
Sonke GS; Heskes T; Verbeek AL; de la Rosette JJ; Kiemeney LA
J Urol; 2000 Jan; 163(1):300-5. PubMed ID: 10604380
[TBL] [Abstract][Full Text] [Related]
38. Implementation of an artificial neuronal network to predict shunt necessity in carotid surgery.
Aleksic M; Luebke T; Heckenkamp J; Gawenda M; Reichert V; Brunkwall J
Ann Vasc Surg; 2008 Sep; 22(5):635-42. PubMed ID: 18761224
[TBL] [Abstract][Full Text] [Related]
39. Application of an artificial neural network to high-resolution CT: usefulness in differential diagnosis of diffuse lung disease.
Fukushima A; Ashizawa K; Yamaguchi T; Matsuyama N; Hayashi H; Kida I; Imafuku Y; Egawa A; Kimura S; Nagaoki K; Honda S; Katsuragawa S; Doi K; Hayashi K
AJR Am J Roentgenol; 2004 Aug; 183(2):297-305. PubMed ID: 15269016
[TBL] [Abstract][Full Text] [Related]
40. [Perforated gastroduodenal ulcer. II. Risk analysis in the prognostic evaluation of the postoperative course].
Krenzien J; Röding H; Rudtke B
Zentralbl Chir; 1990; 115(8):471-81. PubMed ID: 2360374
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
