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Items: 1 to 20 of 131

1.

Comparisons of prediction models of quality of life after laparoscopic cholecystectomy: a longitudinal prospective study.

Shi HY, Lee HH, Tsai JT, Ho WH, Chen CF, Lee KT, Chiu CC.

PLoS One. 2012;7(12):e51285. doi: 10.1371/journal.pone.0051285. Epub 2012 Dec 28.

2.

Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models.

Shi HY, Tsai JT, Chen YM, Culbertson R, Chang HT, Hou MF.

Breast Cancer Res Treat. 2012 Aug;135(1):221-9. doi: 10.1007/s10549-012-2174-6. Epub 2012 Jul 27.

PMID:
22836876
3.

Comparisons of prediction models of myofascial pain control after dry needling: a prospective study.

Huang YT, Neoh CA, Lin SY, Shi HY.

Evid Based Complement Alternat Med. 2013;2013:478202. doi: 10.1155/2013/478202. Epub 2013 Jun 18.

4.

Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery.

Shi HY, Lee KT, Lee HH, Ho WH, Sun DP, Wang JJ, Chiu CC.

PLoS One. 2012;7(4):e35781. doi: 10.1371/journal.pone.0035781. Epub 2012 Apr 26.

5.

Predicting quality of life after breast cancer surgery using ANN-based models: performance comparison with MR.

Tsai JT, Hou MF, Chen YM, Wan TT, Kao HY, Shi HY.

Support Care Cancer. 2013 May;21(5):1341-50. doi: 10.1007/s00520-012-1672-8. Epub 2012 Dec 1.

PMID:
23203653
6.

Support vector machine regression (LS-SVM)--an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data?

Balabin RM, Lomakina EI.

Phys Chem Chem Phys. 2011 Jun 28;13(24):11710-8. doi: 10.1039/c1cp00051a. Epub 2011 May 19.

PMID:
21594265
7.

Prediction of intrinsic solubility of generic drugs using MLR, ANN and SVM analyses.

Louis B, Agrawal VK, Khadikar PV.

Eur J Med Chem. 2010 Sep;45(9):4018-25. doi: 10.1016/j.ejmech.2010.05.059. Epub 2010 Jun 4.

PMID:
20584562
8.

Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies.

Verplancke T, Van Looy S, Benoit D, Vansteelandt S, Depuydt P, De Turck F, Decruyenaere J.

BMC Med Inform Decis Mak. 2008 Dec 5;8:56. doi: 10.1186/1472-6947-8-56.

9.

QSPR study of Setschenow constants of organic compounds using MLR, ANN, and SVM analyses.

Xu J, Wang L, Wang L, Shen X, Xu W.

J Comput Chem. 2011 Nov 30;32(15):3241-52. doi: 10.1002/jcc.21907. Epub 2011 Aug 12.

PMID:
21837634
11.
12.
13.

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. doi: 10.1016/j.artmed.2009.05.002. Epub 2009 Jun 12.

PMID:
19524413
14.

Predictions of chromatographic retention indices of alkylphenols with support vector machines and multiple linear regression.

Fatemi MH, Baher E, Ghorbanzade'h M.

J Sep Sci. 2009 Dec;32(23-24):4133-42. doi: 10.1002/jssc.200900373.

PMID:
19937857
15.

Conversion of laparoscopic cholecystectomy to open cholecystectomy in acute cholecystitis: artificial neural networks improve the prediction of conversion.

Eldar S, Siegelmann HT, Buzaglo D, Matter I, Cohen A, Sabo E, Abrahamson J.

World J Surg. 2002 Jan;26(1):79-85. Epub 2001 Nov 26.

PMID:
11898038
16.

Prediction of conversion of laparoscopic cholecystectomy to open surgery with artificial neural networks.

Gholipour C, Fakhree MB, Shalchi RA, Abbasi M.

BMC Surg. 2009 Aug 21;9:13. doi: 10.1186/1471-2482-9-13.

17.

Comparative chemometric modeling of cytochrome 3A4 inhibitory activity of structurally diverse compounds using stepwise MLR, FA-MLR, PLS, GFA, G/PLS and ANN techniques.

Roy K, Pratim Roy P.

Eur J Med Chem. 2009 Jul;44(7):2913-22. doi: 10.1016/j.ejmech.2008.12.004. Epub 2008 Dec 16.

PMID:
19128860
18.

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 Jr, Gonet JA, Wong RC.

Lancet. 2003 Oct 18;362(9392):1261-6.

PMID:
14575969
19.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, Tropsha A.

J Med Chem. 2006 Nov 30;49(24):7169-81.

PMID:
17125269
20.

Application of Artificial Neural Network and Support Vector Machines in Predicting Metabolizable Energy in Compound Feeds for Pigs.

Ahmadi H, Rodehutscord M.

Front Nutr. 2017 Jun 30;4:27. doi: 10.3389/fnut.2017.00027. eCollection 2017.

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