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Year 2015, Volume: 5 Issue: 2, 169 - 187, 01.12.2015

Abstract

References

  • [1] Ryan, J., (2010), A history of the Internet and the digital future, Reaktion, London, UK.
  • [2] Handbook of Medical Informatics, http://www.mieur.nl/mihandbook/r 3 3/handbook/home.htm.
  • [3] Reichertz, P. L., (2006), Hospital information systems-past, present, future, International Journal of Medical Informatics, vol.75, issues 3-4, pp. 282-289.
  • [4] Burstein, F and Carlsson, S., (2008), Decision support through knowledge management, in Handbook on Decision Support Systems 1: Basic Themes, eds Frada Burstein and Clyde W. Holsapple, pp. 103-120, Springer-Verlag, Berlin Germany.
  • [5] Gaynor, M, Seltzer, M., Moulton, S. and Freedman, J., (2006), A dynamic, data-driven, decision support system for emergency medical services, International Conference on Computational Science, 2, pp. 703-711.
  • [6] Zerger, A. and Smith, D. I., (2003), Impediments to using GIS for real-time disaster decision support, Computers, Environment and Urban Systems, 27, pp. 123-141.
  • [7] Zhu, S., Abraham, J., Paul, S. A., Reddy, M., Yen, J., Pfaff, M. and DeFlitch, C., (2007), R-CASTMED: Applying intelligent agents to support emergency medical decision making teams, in Proc. of the 11th Conference on Artificial Intelligence in Medicine (AIME2007), pp. 34-41, Amsterdam, Netherlands.
  • [8] Yarman, B. S., et al, (2006), Multi-dimensional system approach to assess the outcome of human interacted events, Proceedings of 17th International Symposium of Mathematical Theory of Networks and Systems, pp. 799-802, Kyoto, Japan.
  • [9] Krause, P., Fox, J., O’Neil, M. and Glowinski, A., (1993), Can we formally specify a medical decision support system, IEEE Expert, vol.8, issue: 3, pp. 56-61.
  • [10] G.-P.K. Economou, Goumas, P. D. and Spiropoulos, K., (1996), A novel medical decision support system, Computing & Control Engineering Journal, vol.7, issue: 4, pp. 177-183.
  • [11] Faust, O., Acharya, U. R. and Tamura, T., (2012), Formal design methods for reliable computer-aided diagnosis: A review, IEEE Reviews in Biomedical Engineering, vol.5, pp. 15-28.
  • [12] G.-P.K. Economou, Lymberopoulos, D., Karavatselou, E. and Chassomeris, C., (2001), A new concept toward computer-aided medical diagnosis-a prototype implementation addressing pulmonary diseases, IEEE Transactions on Information Technology in Biomedicine, vol. 5, issue: 1, pp. 55-65.
  • [13] Hudson, D. L. and Cohen, M. E., (2010), Diagnostic models based on personalized analysis of trends (PAT), IEEE Transactions on Information Technology in Biomedicine, vol.14, issue: 4, pp. 941-948.
  • [14] Mc Intyre, R., Stiegmann, G. V. and Eiseman, B., (2004), Surgical decision making, Saunders.
  • [15] Harris Interactive. Ethics and the Internet (2000) : Consumers vs. Webmasters, Internet Healthcare Coalition, and National Mental Health Association.
  • [16] Kaplan, B. and Brennan, P. F., (2001), Consumer informatics supporting patients as co-producers of quality, J Am Med Inform Assoc., 8:309-16.
  • [17] Jadad, A. R., Haynes, R. B., Hunt, D. and Browman, G. P., (2000), The internet and evidencebased decision-making: A needed synergy for efficient knowledge management in health care, CMAJ, 162(3):362-5.
  • [18] Yarman, S., Alagol, F., Gurbuz, L., Kapran, Y. and Tanakol, R., (2001), Association of fallot tetralogy with Carney’s complex, Irish Medical Journal, vol.94, number 10.
  • [19] Fielding, R. T. and Kaiser, G., (1997), The Apache HTTP server project, IEEE Internet Computing, vol.1, issue: 4, pp.88-90.
  • [20] Sklar, D. and Trachtenberg, A., (2014), PHP cookbook, 3rd edition, Solutions & Examples for PHP Programmers, O’Reilly Media.
  • [21] History of MySQL. MySQL 5.1 Reference manual. MySQL AB, (2011).
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  • [23] Bishop, C. M., (2007), Pattern recognition and machine learning, Springer.
  • [24] Pavlidis, T., (1977), Structural pattern recognition, Springer-Verlag, New York.
  • [25] Wang, T. P., Vagnucci, A. H., Pratt, V. and Li, C. C., (1989), Feature extraction and pattern classification of hormonal time series, Engineering in Medicine and Biology Society, vol.2, pp. 752- 753.
  • [26] Mindermann, T. and Wilson, C. B., (1999), Age-related occurrence of pituitary adenomas, Clin Endocrinol, 41, pp. 359-364.
  • [27] Melmed, S., (2010), The pituitary, 3rd edition, Academic Press.
  • [28] Melmed, S., Jameson, J. L. and Groot, L. D., (2013), Endocrinology adult and pediatric: Neuroendocrinology and the pituitary gland, Saunders.
  • [29] Canobbio, M., (2005), Mosby’s handbook of patient teaching, Mosby.
  • [30] Nabarro, J. D., (1987), Acromegaly, Clin Endocrinol (Oxf ) 26:481.512.
  • [31] Ezzat, S., Forster, M. J., Berchtold, P., Redelmeier, D. A., Boerlin, V. and Harris, A. G., (1994), Acromegaly, clinical and biochemical features in 500 patients, Medicine (Baltimore) 73:233.240.
  • [32] Molitch, M. E., (1992), Clinical manifestations of acromegaly, Endocrinol Metab Clin North Am 21:597. 614.
  • [33] Podgorski, M., Robinson, B., Weissberger, A., Stiel, J., Wang, S. and Brooks, P. M., (1988), Articular manifestations of acromegaly, Aust N Z J Med 18:28.35.
  • [34] Layton, M. W., Fudman, E. J., Barkan, A., Braunstein, E. M. and Fox, I. H., (1988), Acromegalic arthropathy. Characteristics and response to therapy, Arthritis Rheum 31:1022.1027.
  • [35] Schlechte, J. A., (2003), Prolactinoma, New England Journal of Medicine 349.21, pp. 2035-2041.
  • [36] Chanson, P. and Salenave, S., (2008), Acromegaly, Orphanet journal of rare diseases 3.1, 17.
  • [37] Gencturk, B., Nabiyev, V. V. and Ustubioglu, A., (2013), Acromegaly pre-diagnosis based on principal component and linear discriminant analysis, Signal Processing and Communications Applications Conference (SIU), pp. 1-4.
  • [38] Gencturk, B., Nabiyev, V. V., Ustubioglu, A. and Ketenci, S., (2013), Automated pre-diagnosis of acromegaly disease using local binary patterns and its variants, 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 817-821.
  • [39] Kırma, C., (1994), Seksenbir akromegalik hastanın retrospektif de¯erlendirilmesi, Istanbul Universitesi ˆ Tıp Fakˆultesi, I¸c Hastalıkları ABD, Istanbul.
  • [40] Demirel, ., (1991), 51 cushing sendromu olgusunda klinik, laboratuar ve tedavi modellerinin deerlendirilmesi”, Istanbul Universitesi, Tp Fakltesi, I Hastalklar ABD, Istanbul.
  • [41] Cushing sendromu: 45 Hastanin klinik ve laboratuar bulgular ve tedavi sonular”, http://www.istanbul.edu.tr/istanbultip/mecmua/fakmecmua/sayi3-99/01.html
  • [42] Cushing’s syndrome, http://www.endocrineweb.com/obesity.html
  • [43] Ursino, M., Giannessi, M., Frapparelli, M. and Magosso, E., (2009), Effect of cushing response on systemic arterial pressure, IEEE Engineering in Medicine and Biology Magazine, vol.28, issue: 6, pp. 63-71.
  • [44] Vagnucci, A. H., Wang, T. P., Pratt, V. and Li, C.C., (1991), Classification of plasma cortisol patterns in normal subjects and in Cushing’s syndrome, IEEE Transactions on Biomedical Engineering, vol.38, issue: 2, pp. 113-125.
  • [45] Disorders of the anterior pituitary and Hypothalamus. Eds; S. Melmed and J. L. Jameson, (2011), Harrison’s Principles of Internal Medicine, 16th Edition, Eds; D. L. Kasper, E. Braunwald, A. S. Fauci, S. L. Hauser, D. L. Longo and J. L. Jameson, p.2093, McGraw-Hill, New York.
  • [46] Murphy, F. Y., Vesely, D. L., Jordan, R. M., Flamingan, S. and Kohler, P. O., (1987), Giant invasive prolactinomas, American Journal Med, 88, pp. 995-1002.
  • [47] Acharya, S. V., Gopal, R. A., Menon, P. S., Bandgar, T. R. and Shah, N. S., (2010), Giant prolactinoma and effectiveness of medical management, Endocrinol Pract, 16, pp. 42-46.
  • [48] Basic summary for prolactinoma, http://www.wrongdiagnosis.com/p/prolactinoma/basics.htm.

An Intelligent Decision Support Tool for Early Diagnosis of Functional Pituitary Adenomas

Year 2015, Volume: 5 Issue: 2, 169 - 187, 01.12.2015

Abstract

In this work, a web based integrated Medical Decision Support System MDSS tool for mainly early diagnosis of functional pituitary adenomas i.e., somatotrophinoma, corticotrophinoma and prolactinoma is developed. In the MDSS tool, hormone diseases are described by means of well-classified set of attributes generated from the typical sign and symptoms of disorders.The proposed tool is based on a stationary linear stochastic system model which specifically predicts the selected hormone diseases employing certain system parameters. The MDSS tool is user friendly which includes questions and answers at the opening session of the self-test. Questions and answers session will be completed by “yes” or “no” type of simple-responses. Based on our clinical results, MDSS tool yields more than 99% correct decisions on the selected hormone diseases. It is expected that effective use of the proposed MDSS tool will save substantial amount of valuable time of an expert endocrinologists and minimizes the cost of diagnosis. Furthermore, it will provide the opportunity for early diagnosis for the patient and the expert medical doctor to take the necessary preventive measures

References

  • [1] Ryan, J., (2010), A history of the Internet and the digital future, Reaktion, London, UK.
  • [2] Handbook of Medical Informatics, http://www.mieur.nl/mihandbook/r 3 3/handbook/home.htm.
  • [3] Reichertz, P. L., (2006), Hospital information systems-past, present, future, International Journal of Medical Informatics, vol.75, issues 3-4, pp. 282-289.
  • [4] Burstein, F and Carlsson, S., (2008), Decision support through knowledge management, in Handbook on Decision Support Systems 1: Basic Themes, eds Frada Burstein and Clyde W. Holsapple, pp. 103-120, Springer-Verlag, Berlin Germany.
  • [5] Gaynor, M, Seltzer, M., Moulton, S. and Freedman, J., (2006), A dynamic, data-driven, decision support system for emergency medical services, International Conference on Computational Science, 2, pp. 703-711.
  • [6] Zerger, A. and Smith, D. I., (2003), Impediments to using GIS for real-time disaster decision support, Computers, Environment and Urban Systems, 27, pp. 123-141.
  • [7] Zhu, S., Abraham, J., Paul, S. A., Reddy, M., Yen, J., Pfaff, M. and DeFlitch, C., (2007), R-CASTMED: Applying intelligent agents to support emergency medical decision making teams, in Proc. of the 11th Conference on Artificial Intelligence in Medicine (AIME2007), pp. 34-41, Amsterdam, Netherlands.
  • [8] Yarman, B. S., et al, (2006), Multi-dimensional system approach to assess the outcome of human interacted events, Proceedings of 17th International Symposium of Mathematical Theory of Networks and Systems, pp. 799-802, Kyoto, Japan.
  • [9] Krause, P., Fox, J., O’Neil, M. and Glowinski, A., (1993), Can we formally specify a medical decision support system, IEEE Expert, vol.8, issue: 3, pp. 56-61.
  • [10] G.-P.K. Economou, Goumas, P. D. and Spiropoulos, K., (1996), A novel medical decision support system, Computing & Control Engineering Journal, vol.7, issue: 4, pp. 177-183.
  • [11] Faust, O., Acharya, U. R. and Tamura, T., (2012), Formal design methods for reliable computer-aided diagnosis: A review, IEEE Reviews in Biomedical Engineering, vol.5, pp. 15-28.
  • [12] G.-P.K. Economou, Lymberopoulos, D., Karavatselou, E. and Chassomeris, C., (2001), A new concept toward computer-aided medical diagnosis-a prototype implementation addressing pulmonary diseases, IEEE Transactions on Information Technology in Biomedicine, vol. 5, issue: 1, pp. 55-65.
  • [13] Hudson, D. L. and Cohen, M. E., (2010), Diagnostic models based on personalized analysis of trends (PAT), IEEE Transactions on Information Technology in Biomedicine, vol.14, issue: 4, pp. 941-948.
  • [14] Mc Intyre, R., Stiegmann, G. V. and Eiseman, B., (2004), Surgical decision making, Saunders.
  • [15] Harris Interactive. Ethics and the Internet (2000) : Consumers vs. Webmasters, Internet Healthcare Coalition, and National Mental Health Association.
  • [16] Kaplan, B. and Brennan, P. F., (2001), Consumer informatics supporting patients as co-producers of quality, J Am Med Inform Assoc., 8:309-16.
  • [17] Jadad, A. R., Haynes, R. B., Hunt, D. and Browman, G. P., (2000), The internet and evidencebased decision-making: A needed synergy for efficient knowledge management in health care, CMAJ, 162(3):362-5.
  • [18] Yarman, S., Alagol, F., Gurbuz, L., Kapran, Y. and Tanakol, R., (2001), Association of fallot tetralogy with Carney’s complex, Irish Medical Journal, vol.94, number 10.
  • [19] Fielding, R. T. and Kaiser, G., (1997), The Apache HTTP server project, IEEE Internet Computing, vol.1, issue: 4, pp.88-90.
  • [20] Sklar, D. and Trachtenberg, A., (2014), PHP cookbook, 3rd edition, Solutions & Examples for PHP Programmers, O’Reilly Media.
  • [21] History of MySQL. MySQL 5.1 Reference manual. MySQL AB, (2011).
  • [22] Duda, R. O., Hart, P. E and Stork,D. G., (2000), Pattern classification, 2nd Edition, Wiley.
  • [23] Bishop, C. M., (2007), Pattern recognition and machine learning, Springer.
  • [24] Pavlidis, T., (1977), Structural pattern recognition, Springer-Verlag, New York.
  • [25] Wang, T. P., Vagnucci, A. H., Pratt, V. and Li, C. C., (1989), Feature extraction and pattern classification of hormonal time series, Engineering in Medicine and Biology Society, vol.2, pp. 752- 753.
  • [26] Mindermann, T. and Wilson, C. B., (1999), Age-related occurrence of pituitary adenomas, Clin Endocrinol, 41, pp. 359-364.
  • [27] Melmed, S., (2010), The pituitary, 3rd edition, Academic Press.
  • [28] Melmed, S., Jameson, J. L. and Groot, L. D., (2013), Endocrinology adult and pediatric: Neuroendocrinology and the pituitary gland, Saunders.
  • [29] Canobbio, M., (2005), Mosby’s handbook of patient teaching, Mosby.
  • [30] Nabarro, J. D., (1987), Acromegaly, Clin Endocrinol (Oxf ) 26:481.512.
  • [31] Ezzat, S., Forster, M. J., Berchtold, P., Redelmeier, D. A., Boerlin, V. and Harris, A. G., (1994), Acromegaly, clinical and biochemical features in 500 patients, Medicine (Baltimore) 73:233.240.
  • [32] Molitch, M. E., (1992), Clinical manifestations of acromegaly, Endocrinol Metab Clin North Am 21:597. 614.
  • [33] Podgorski, M., Robinson, B., Weissberger, A., Stiel, J., Wang, S. and Brooks, P. M., (1988), Articular manifestations of acromegaly, Aust N Z J Med 18:28.35.
  • [34] Layton, M. W., Fudman, E. J., Barkan, A., Braunstein, E. M. and Fox, I. H., (1988), Acromegalic arthropathy. Characteristics and response to therapy, Arthritis Rheum 31:1022.1027.
  • [35] Schlechte, J. A., (2003), Prolactinoma, New England Journal of Medicine 349.21, pp. 2035-2041.
  • [36] Chanson, P. and Salenave, S., (2008), Acromegaly, Orphanet journal of rare diseases 3.1, 17.
  • [37] Gencturk, B., Nabiyev, V. V. and Ustubioglu, A., (2013), Acromegaly pre-diagnosis based on principal component and linear discriminant analysis, Signal Processing and Communications Applications Conference (SIU), pp. 1-4.
  • [38] Gencturk, B., Nabiyev, V. V., Ustubioglu, A. and Ketenci, S., (2013), Automated pre-diagnosis of acromegaly disease using local binary patterns and its variants, 36th International Conference on Telecommunications and Signal Processing (TSP), pp. 817-821.
  • [39] Kırma, C., (1994), Seksenbir akromegalik hastanın retrospektif de¯erlendirilmesi, Istanbul Universitesi ˆ Tıp Fakˆultesi, I¸c Hastalıkları ABD, Istanbul.
  • [40] Demirel, ., (1991), 51 cushing sendromu olgusunda klinik, laboratuar ve tedavi modellerinin deerlendirilmesi”, Istanbul Universitesi, Tp Fakltesi, I Hastalklar ABD, Istanbul.
  • [41] Cushing sendromu: 45 Hastanin klinik ve laboratuar bulgular ve tedavi sonular”, http://www.istanbul.edu.tr/istanbultip/mecmua/fakmecmua/sayi3-99/01.html
  • [42] Cushing’s syndrome, http://www.endocrineweb.com/obesity.html
  • [43] Ursino, M., Giannessi, M., Frapparelli, M. and Magosso, E., (2009), Effect of cushing response on systemic arterial pressure, IEEE Engineering in Medicine and Biology Magazine, vol.28, issue: 6, pp. 63-71.
  • [44] Vagnucci, A. H., Wang, T. P., Pratt, V. and Li, C.C., (1991), Classification of plasma cortisol patterns in normal subjects and in Cushing’s syndrome, IEEE Transactions on Biomedical Engineering, vol.38, issue: 2, pp. 113-125.
  • [45] Disorders of the anterior pituitary and Hypothalamus. Eds; S. Melmed and J. L. Jameson, (2011), Harrison’s Principles of Internal Medicine, 16th Edition, Eds; D. L. Kasper, E. Braunwald, A. S. Fauci, S. L. Hauser, D. L. Longo and J. L. Jameson, p.2093, McGraw-Hill, New York.
  • [46] Murphy, F. Y., Vesely, D. L., Jordan, R. M., Flamingan, S. and Kohler, P. O., (1987), Giant invasive prolactinomas, American Journal Med, 88, pp. 995-1002.
  • [47] Acharya, S. V., Gopal, R. A., Menon, P. S., Bandgar, T. R. and Shah, N. S., (2010), Giant prolactinoma and effectiveness of medical management, Endocrinol Pract, 16, pp. 42-46.
  • [48] Basic summary for prolactinoma, http://www.wrongdiagnosis.com/p/prolactinoma/basics.htm.
There are 48 citations in total.

Details

Primary Language English
Journal Section Research Article
Authors

I. Z. Gökbay

S. L. Karaman This is me

S. Yarman This is me

B. S. Yarman This is me

Publication Date December 1, 2015
Published in Issue Year 2015 Volume: 5 Issue: 2

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