Abstract
Hypertension is estimated to be the third leading cause of death worldwide and its management is based on guidelines regarding diagnosis, evaluation, risk assessment, treatment and continuing care. This paper presents an intelligent decision support system, which operationalises algorithms for hypertension management using intelligent technologies. PIESYS encourages blood pressure control and recommends guideline-concordant choice of drug therapy in relation to co morbid diseases. Because evidence for best management of hypertension is mostly individualized, PIESYS is designed to help clinical experts to customize their therapeutic strategy with the use of the Patient Response Database (PRDB) incorporating initial or current data with patient responses or side effects, providing response-adaptive continual care. Together with PRDB, PIESYS uses an independent module, called Computerized Patient Model (CPM), reflecting patient’s current state, which affects therapy or care modifications for hypertension management. PIESYS introduces personalised (patient-centric) approach in health care systems in contrast to guideline-dependent classical ones.
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References
Institute for Clinical Systems Improvement (ICSI). Hypertension diagnosis and treatment. Bloomington (MN): Institute for Clinical Systems Improvement (ICSI), p. 53 (2005)
Johnson, P.D., Tu, S., Booth, N., Sugden, B., Purves, I.: Using scenarios in chronic disease management guidelines for primary care. In: Proc. AMIA Symp., pp. 389–393 (2000)
Peleska, J., Svejda, D., Zvarova, J.: Computer supported decision making in therapy of arterial hypertension. Int. J. Med. Inform. 45(1-2), 25–29 (1997)
Takahashi, E., Yoshida, K., Izuno, T., Miyakawa, M., Sugimori, H.: Protocol care for hypertension supported by an expert system. Medinfo. 8(2), 954 (1995)
van der Lei, J., van der Does, E., Man in ’t Veld, A.J., Musen, M.A., van Bemmel, J.H.: Response of general practitioners to computer-generated critiques of hypertension therapy. Methods. Inf. Med. 32(2), 146–153 (1993)
Blinowska, A., Chatellier, G., Bernier, J., Lavril, M.: Bayesian statistics as applied to hypertension diagnosis. IEEE Trans Biomed Eng 38(7), 699–706 (1991)
Degoulet, P., Chatellier, G., Devries, C., Lavril, M., Menard, J.: Computer-assisted techniques for evaluation and treatment of hypertensive patients. Am. J. Hypertens 3(2), 156–163 (1990)
Devries, C., Degoulet, P., Jeunemaitre, X., Sauquet, D., Morice, V., Chatellier, G., Aime, F., Menard, J.: Integrating management and expertise in a computerised system for hypertensive patients. Nephrol Dial Transplant 2(5), 327–331 (1987)
Jeunemaitre, X., Degoulet, P., Morice, V., Chatellier, G., Devries, C., Plouin, P.F., Boisvieux, J.F., Menard, J.: Testing an expert system for hypertension. Arch. Mal. Coeur. Vaiss 79(6), 808–812 (1986)
Siepmann, J.P., Bachman, J.W.: HTN-APT: Computer aid in hypertension management. J. Fam. Pract 24(3), 313–316 (1987)
Seroussi, B., Bouaud, J., Chatellier, G.: Modeling patient-specific therapeutic strategy in the guideline-based management of a chronic disease. Stud Health Technol Inform 95, 537–542 (2003)
Montgomery, A., Fahey, T., Peters, T., MacIntosh, C., Sharp, D.: Evaluation of computer based clinical decision support system and risk chart for management of hypertension in primary care: Randomised controlled trial. BMJ 2000 320, 686–690
de Clercq, P.A.: Guideline-based Decision Support in Medicine Modelling Guidelines for the Development and Application of Clinical Decision Support Systems NUGI 981, Technische Universiteit Eindhoven (2003)
Negnevitsky, M.: Artificial Intelligence. A guide to Intelligent Systems. Addison Wesley, Reading (2002)
Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. The sixth report of the Joint National Committee on Prevention, Detection,Evaluation, and Treatment of High Blood Pressure. Arch. Intern. Med., 157 p. 2413–2446 (1997)
World Health Organisation - International Society of Hypertension guidelines for the management of hypertension. J. Hypertens 1999 17, 151–183 (1999)
Koutsojannis, C., Hatzilygeroudis, I.: Fuzzy-Evolutionary Synergism in an Intelligent Medical Diagnosis System. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4252, pp. 1313–1322. Springer, Heidelberg (2006)
Gamberger, D., Krstacic, G., Smuc, T.: Medical Expert Evaluation of Machine Learning Results for a Coronary Heart Disease Database. In: Brause, R., Hanisch, E. (eds.) ISMDA 2000. LNCS, vol. 1933, pp. 119–122. Springer, Heidelberg (2000)
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Koutsojannis, C., Hatzilygeroudis, I. (2008). PIESYS: A Patient Model-Based Intelligent System for Continuing Hypertension Management. In: Riaño, D. (eds) Knowledge Management for Health Care Procedures. K4CARE 2007. Lecture Notes in Computer Science(), vol 4924. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78624-5_11
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DOI: https://doi.org/10.1007/978-3-540-78624-5_11
Publisher Name: Springer, Berlin, Heidelberg
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