메뉴 건너뛰기




Volumn 36, Issue 3, 2012, Pages 2057-2061

Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data

Author keywords

Artificial neural network; Diagnosis; Iron level; Modeling; Neuro fuzzy

Indexed keywords

ADAPTIVE NEURO FUZZY INFERENCE SYSTEM; ADULT; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; FEMALE; FUZZY SYSTEM; HUMAN; INFORMATION PROCESSING; IRON BLOOD LEVEL; IRON DEFICIENCY ANEMIA; LOGISTIC REGRESSION ANALYSIS; MAJOR CLINICAL STUDY; MALE;

EID: 85027932488     PISSN: 01485598     EISSN: 1573689X     Source Type: Journal    
DOI: 10.1007/s10916-011-9668-3     Document Type: Article
Times cited : (42)

References (24)
  • 2
    • 0035150473 scopus 로고    scopus 로고
    • IV, Iron deficiency and reduced work capacity: A critical review of the research to determine a causal relationship
    • discussion 688S-90S
    • Haas, J. D., and Brownlie, T., IV, Iron deficiency and reduced work capacity: a critical review of the research to determine a causal relationship. J. Nutr. 131(2 suppl):676S-88S, 2001. discussion 688S-90S.
    • (2001) J. Nutr. , vol.131 , Issue.2 SUPPL.
    • Haas, J.D.1    Brownlie, T.2
  • 3
    • 0034995398 scopus 로고    scopus 로고
    • Iron deficiency and cognitive achievement among school-aged children and adolescents in the United States
    • DOI 10.1542/peds.107.6.1381
    • Halterman, J. S., Kaczorowski, J. M., Aligne, C. A., Auinger, P., and Szilagyi, P. G., Iron deficiency and cognitive achievement among school-aged children and adolescents in the United States. Pediatrics 107:1381-6, 2001. (Pubitemid 32525220)
    • (2001) Pediatrics , vol.107 , Issue.6 , pp. 1381-1386
    • Halterman, J.S.1    Kaczorowski, J.M.2    Aligne, C.A.3    Auinger, P.4    Szilagyi, P.G.5
  • 4
    • 0024442345 scopus 로고
    • Iron deficiency: Definition and diagnosis
    • Cook, J. D., and Skikne, B. S., Iron deficiency: definition and diagnosis. J. Intern. Med. 226(5):349-55, 1989. (Pubitemid 19269639)
    • (1989) Journal of Internal Medicine , vol.226 , Issue.5 , pp. 349-355
    • Cook, J.D.1    Skikne, B.S.2
  • 5
    • 0030997579 scopus 로고    scopus 로고
    • The laboratory assessment of iron status: An update
    • Worwood, M., The laboratory assessment of iron status: an update. Clin. Chim. Acta 259:3-23, 1997.
    • (1997) Clin. Chim. Acta , vol.259 , pp. 3-23
    • Worwood, M.1
  • 6
    • 0028885032 scopus 로고
    • Introduction to neural networks
    • Cross, S. S., Harrison, R. F., and Kennedy, R. L., Introduction to neural networks. Lancet 346:1075-1079, 1995.
    • (1995) Lancet , vol.346 , pp. 1075-1079
    • Cross, S.S.1    Harrison, R.F.2    Kennedy, R.L.3
  • 7
    • 34548461523 scopus 로고    scopus 로고
    • Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network
    • DOI 10.1016/j.biosystems.2006.12.004, PII S0303264706002905
    • Dariani, S., Keshavarz, M., Parviz, M., Raoufy, M. R., and Gharibzadeh, S., Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network. Biosystems 90(2):529-34, 2007. (Pubitemid 47364812)
    • (2007) BioSystems , vol.90 , Issue.2 , pp. 529-534
    • Dariani, S.1    Keshavarz, M.2    Parviz, M.3    Raoufy, M.R.4    Gharibzadeh, S.5
  • 8
    • 0028820429 scopus 로고
    • Artificial neural networks for decision support in clinical medicine
    • Forsstrom, J. J., and Dalton, K. J., Artificial neural networks for decision support in clinical medicine. Ann. Med. 27:509-17, 1995.
    • (1995) Ann. Med. , vol.27 , pp. 509-517
    • Forsstrom, J.J.1    Dalton, K.J.2
  • 9
    • 0035137729 scopus 로고    scopus 로고
    • Introduction to artificial neural networks for physicians: Taking the lid off the black box
    • DOI 10.1002/1097-0045(200101)46:1<39::AID-PROS1006>3.0.CO;2-M
    • Rodvold, D. M., McLeod, D. G., Brandt, J. M., Snow, P. B., and Murphy, G. P., Introduction to artificial neural networks for physicians: taking the lid off the black box. Prostate 46:39-44, 2001. (Pubitemid 32157774)
    • (2001) Prostate , vol.46 , Issue.1 , pp. 39-44
    • Rodvold, D.M.1    McLeod, D.G.2    Brandt, J.M.3    Snow, P.B.4    Murphy, G.P.5
  • 10
    • 0035479038 scopus 로고    scopus 로고
    • Artificial nonmonotonic neural networks
    • DOI 10.1016/S0004-3702(01)00126-6, PII S0004370201001266
    • Boutsinas, B., and Vrahatis, M., Artificial nonmonotonic neural networks. Artif. Intell. 132:1-38, 2001. (Pubitemid 32888153)
    • (2001) Artificial Intelligence , vol.132 , Issue.1 , pp. 1-38
    • Boutsinas, B.1    Vrahatis, M.N.2
  • 11
    • 0036127092 scopus 로고    scopus 로고
    • A review of evidence of health benefit from artificial neural networks in medical intervention
    • PII S0893608001001113
    • Lisboa, P., A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. 15:11-39, 2002. (Pubitemid 34234792)
    • (2002) Neural Networks , vol.15 , Issue.1 , pp. 11-39
    • Lisboa, P.J.G.1
  • 14
    • 0032745223 scopus 로고    scopus 로고
    • Artificial neural networks in laboratory medicine and medical outcome prediction
    • DOI 10.1515/CCLM.1999.128
    • Tafeit, E., and Reibnegger, G., Artificial neural networks in laboratory medicine and medical outcome prediction. Clin. Chem. Lab. Med. 37:845-53, 1999. (Pubitemid 29535055)
    • (1999) Clinical Chemistry and Laboratory Medicine , vol.37 , Issue.9 , pp. 845-853
    • Tafeit, E.1    Reibnegger, G.2
  • 15
    • 16544393180 scopus 로고    scopus 로고
    • Neural networks as robust tools in drug lead discovery and development
    • Winkler, D. A., Neural networks as robust tools in drug lead discovery and development. Mol. Biotechnol. 27:139-68, 2004.
    • (2004) Mol. Biotechnol. , vol.27 , pp. 139-168
    • Winkler, D.A.1
  • 16
    • 0025689918 scopus 로고
    • Artificial neural networks and their use in quantitative pathology
    • Dytch, H. E., and Wied, G. L., Artificial neural networks and their use in quantitative pathology. Anal. Quant. Cytol. Histol. 12:379-93, 1990.
    • (1990) Anal. Quant. Cytol. Histol. , vol.12 , pp. 379-393
    • Dytch, H.E.1    Wied, G.L.2
  • 17
    • 0030297904 scopus 로고    scopus 로고
    • Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes
    • DOI 10.1016/S0895-4356(96)00002-9, PII S0895435696000029
    • Tu, J. V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49:1225-1231, 1996. (Pubitemid 26386228)
    • (1996) Journal of Clinical Epidemiology , vol.49 , Issue.11 , pp. 1225-1231
    • Tu, J.V.1
  • 18
    • 15244347342 scopus 로고    scopus 로고
    • 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, H. E., Ghodsi, M., and Ketabchi, E., Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med. Inform. Decis. Mak. 5:3, 2005.
    • (2005) BMC Med. Inform. Decis. Mak. , vol.5 , pp. 3
    • Eftekhar, B.1    Mohammad, K.2    Ardebili, H.E.3    Ghodsi, M.4    Ketabchi, E.5
  • 19
    • 79952449677 scopus 로고    scopus 로고
    • A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: Artificial neural network approach
    • Epub 2009 Jul 21
    • Raoufy, M. R., Vahdani, P., Alavian, S. M., Fekri, S., Eftekhari, P., and Gharibzadeh, S., A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: artificial neural network approach. J. Med. Syst. 35(1):121-6, 2011. Epub 2009 Jul 21.
    • (2011) J. Med. Syst. , vol.35 , Issue.1 , pp. 121-126
    • Raoufy, M.R.1    Vahdani, P.2    Alavian, S.M.3    Fekri, S.4    Eftekhari, P.5    Gharibzadeh, S.6
  • 20
    • 0020083498 scopus 로고
    • The meaning and use of the area under a receiver operating characteristic (ROC) curve
    • Hanley, J. A., and McNeil, B. J., The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29-36, 1982. (Pubitemid 12142173)
    • (1982) Radiology , vol.143 , Issue.1 , pp. 29-36
    • Hanley, J.A.1    McNeil, B.J.2
  • 21
    • 0023890867 scopus 로고
    • Measuring the accuracy of diagnostic systems
    • Swets, J. A., Measuring the accuracy of diagnostic systems. Science 240:1285-1293, 1988.
    • (1988) Science , vol.240 , pp. 1285-1293
    • Swets, J.A.1
  • 22
    • 0036363091 scopus 로고    scopus 로고
    • Neural network modeling to predict the hypnotic effect of propofol bolus induction
    • Lin, C. S., Li, Y. C., Mok, M. S., Wu, C. C., Chiu, H. W., and Lin, Y. H., Neural network modeling to predict the hypnotic effect of propofol bolus induction. Proc. AMIA Symp. 450-454, 2002.
    • (2002) Proc. AMIA Symp. , pp. 450-454
    • Lin, C.S.1    Li, Y.C.2    Mok, M.S.3    Wu, C.C.4    Chiu, H.W.5    Lin, Y.H.6
  • 23
    • 69749126033 scopus 로고    scopus 로고
    • Models for prediction of mortality from cirrhosis with special reference to artificial neural network: A critical review
    • DOI 10.1007/s12072-007-9026-1
    • Ghoshal, U. C., and Das, A., Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review. Hepatol. Int. 2(1):31-8, 2008. Epub 2007. (Pubitemid 351250401)
    • (2008) Hepatology International , vol.2 , Issue.1 , pp. 31-38
    • Ghoshal, U.C.1    Das, A.2
  • 24
    • 16544372287 scopus 로고    scopus 로고
    • Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: An artificial neural network prediction model
    • Chong, C. F., Li, Y. C., Wang, T. L., Chang, H., Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model. AMIA Annu. Symp. Proc. 160-164, 2003.
    • (2003) AMIA Annu. Symp. Proc. , pp. 160-164
    • Chong, C.F.1    Li, Y.C.2    Wang, T.L.3    Chang, H.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.