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Yearb Med Inform 1998; 07(01): 385-388
DOI: 10.1055/s-0038-1637899
DOI: 10.1055/s-0038-1637899
Synopsis
Decision Support Systems and Knowledge Processing
Further Information
Address of the author:
Jan A. Kors
Department of Medical Informatics
Faculty of Medicine and Health Sciences
Erasmus University
3000 DR Rotterdam, The Netherlands
Email: [email protected]
Publication History
Publication Date:
10 March 2018 (online)
#
References
- 1 Haddad M, Adlassnig KP, Porenta G. Feasibility analysis of a casebased reasoning system for automated detection of coronary heart disease from myocardial scintigrams.. Artif Intell Med 1997; 9: 61-78.
- 2 Weiss SM, Kulikowski CA. Computer systems that learn.. San Mateo: Morgan Kaufmann; 1991
- 3 Schmidt R, Heindl B, Pollwein B, Gierl L. Multi parametric time course prognoses by means of casebased reasoning and abstractions of data and time.. Med Inform 1997; 22: 237-50
- 4 Duda R, Hart P. Pattern classification and scene analysis.. New York: Wiley; 1973
- 5 Cross SS, Harrison RF, Kennedy RL. Introduction to neural networks.. Lancet 1995; 346: 1075-9
- 6 Wyatt J. Nervous about artificial neural networks?. Lancet 1995; 346: 1175-7
- 7 Lo JY, Baker JA, Kornguth PJ, Iglehart JD. Floyd CE. Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.. Radiology 1997; 203: 159-63
- 8 Brickley MR, Cowpe JG, Shepherd JP. Performance of a computer simulated neural network trained to categorise normal, premalignant and malignant oral smears.. J Oral Pathol Med 1996; 25: 424-8
- 9 Heden B, Ohlsson M, Rittner R, Pahlm O, Haisty WK, Peterson C, Edenbrandt L. Agreement between artificial neural networks and experienced electrocardiographer on electrocardiographic diagnosis of healed myocardial infarction.. J Am Coli Cardiol 1996; 28: 1012-6
- 10 Willems JL, Abreu-Lima C, Arnaud P, Van Bemmel JH, Brohet C, Degani R. et al The diagnostic performance of computer programs for the interpretation of electrocardiograms.. New Engl J Med 1991; 325: 1767-73
- 11 Kennedy RL, Harrison RF, Burton AM, Fraser HS, Hamer WG, MacArthur D, McAllum R, Steedman DJ. An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements.. Comput Methods Prog Biomed 1997; 52: 93-103
- 12 Ohno-Machado L. A comparison of Cox proportional hazards and artificial neural network models for medical prognosis.. Comput Bioi Med 1997; 22: 55-65
- 13 Silver DL, Hurwitz GA. The predictive and explanatory power of inductive decial trees: a comparison with artificial netural networks learning as applied to the noninvasive diagnosis of coronary heart disease.. J Investig Med 1997; 45: 99-108
- 14 Heathfield HA, Wyatt J. Pholosophi for the design and development of clinical decision-support systems.. Meth Inform Med 1993; 32: 1-8
Address of the author:
Jan A. Kors
Department of Medical Informatics
Faculty of Medicine and Health Sciences
Erasmus University
3000 DR Rotterdam, The Netherlands
Email: [email protected]
References
- 1 Haddad M, Adlassnig KP, Porenta G. Feasibility analysis of a casebased reasoning system for automated detection of coronary heart disease from myocardial scintigrams.. Artif Intell Med 1997; 9: 61-78.
- 2 Weiss SM, Kulikowski CA. Computer systems that learn.. San Mateo: Morgan Kaufmann; 1991
- 3 Schmidt R, Heindl B, Pollwein B, Gierl L. Multi parametric time course prognoses by means of casebased reasoning and abstractions of data and time.. Med Inform 1997; 22: 237-50
- 4 Duda R, Hart P. Pattern classification and scene analysis.. New York: Wiley; 1973
- 5 Cross SS, Harrison RF, Kennedy RL. Introduction to neural networks.. Lancet 1995; 346: 1075-9
- 6 Wyatt J. Nervous about artificial neural networks?. Lancet 1995; 346: 1175-7
- 7 Lo JY, Baker JA, Kornguth PJ, Iglehart JD. Floyd CE. Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.. Radiology 1997; 203: 159-63
- 8 Brickley MR, Cowpe JG, Shepherd JP. Performance of a computer simulated neural network trained to categorise normal, premalignant and malignant oral smears.. J Oral Pathol Med 1996; 25: 424-8
- 9 Heden B, Ohlsson M, Rittner R, Pahlm O, Haisty WK, Peterson C, Edenbrandt L. Agreement between artificial neural networks and experienced electrocardiographer on electrocardiographic diagnosis of healed myocardial infarction.. J Am Coli Cardiol 1996; 28: 1012-6
- 10 Willems JL, Abreu-Lima C, Arnaud P, Van Bemmel JH, Brohet C, Degani R. et al The diagnostic performance of computer programs for the interpretation of electrocardiograms.. New Engl J Med 1991; 325: 1767-73
- 11 Kennedy RL, Harrison RF, Burton AM, Fraser HS, Hamer WG, MacArthur D, McAllum R, Steedman DJ. An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements.. Comput Methods Prog Biomed 1997; 52: 93-103
- 12 Ohno-Machado L. A comparison of Cox proportional hazards and artificial neural network models for medical prognosis.. Comput Bioi Med 1997; 22: 55-65
- 13 Silver DL, Hurwitz GA. The predictive and explanatory power of inductive decial trees: a comparison with artificial netural networks learning as applied to the noninvasive diagnosis of coronary heart disease.. J Investig Med 1997; 45: 99-108
- 14 Heathfield HA, Wyatt J. Pholosophi for the design and development of clinical decision-support systems.. Meth Inform Med 1993; 32: 1-8