PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Case-based Planning of Treatment of Infants with Respiratory Failure

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We discuss medical treatment planning in the context of case-based planning, where plans (of treatment) are treated as complex decisions. A plan for a particular case is constructed from known plans for similar training examples. In order to evaluate and improve the prediction quality of complex decisions, we use a method for approximation of similarity measure between plans. The method makes it possible to transform the acquired domain knowledge about similarities of plans, expressed by medical experts in natural language, to a low level language understandable by the system. To accomplish this task, we developed a method for approximation of the ontology of concepts expressed by medical experts. We present two applications of the ontology approximation, namely, for approximation of similarity between patient histories and for approximation of compatibility of patient histories with planned therapies. Next, we use these concept approximations to define two measures on which are based two methods for (plan) therapy prediction. The article includes results of experiments with these methods performed on medical data obtained from Neonatal Intensive Care Unit, First Department of Pediatrics, Polish-American Institute of Pediatrics, Collegium Medicum, Jagiellonian University, Kraków, Poland. The experiments are pertained to the identification of infants' death risk caused by respiratory failure.
Wydawca
Rocznik
Strony
155--172
Opis fizyczny
bibliogr. 31 poz., tab., wykr.
Twórcy
autor
autor
autor
autor
Bibliografia
  • [1] Altman, D. G. : Practical Statistics for Medical Research. Chapman and Hall/CRC, London, 1997.
  • [2] Bazan, J., G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J., J.: Risk Pattern Identification in the Treatment of Infants with Respiratory Failure Through Rough Set Modelling. In: Proceedings of IPMU'2006, Paris, France, July 2-7, 2006, 2650-2657.
  • [3] Bazan, J., G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J., J.: Automatic Planning Based on Rough Set Tools: Towards Supporting Treatment of Infants with Respiratory Failure. In: Proceedings of CSP'2006, Wandlitz, Germany, September 27-29, 2006, 388-399.
  • [4] Bazan, J., G., Kruczek, P., Bazan-Socha, S., Skowron, A., Pietrzyk, J., J.: Automatic Planning of Treatment of Infants with Respiratory Failure Through Rough Set Modelling. In: Proceedings of RSCTC'2006, LNAI 4259, Springer, Heidelberg, 2006, 418-427.
  • [5] Bazan, J. G.: Hierarchical Classifiers for Complex Spatio-Temporal Concepts. Journal LNCS Transactions on Rough Sets, Heidelberg, Springer, LNCS, 2008 (in preparation).
  • [6] Bichindaritz, I., Marling, C.: Case-Based Reasoning in The Health Sciences: What's Next? Artificial Intelligence in Medicine 36, 2006, 127-135.
  • [7] Bichindaritz, I., Montani, S., Portinale, L.: Special issue on case-based reasoning in the health sciences. Volume 28 Number 3 of Applied Intelligence 28(3), 2008, 207-209.
  • [8] Curran, J.S.: From Distributional to Semantic Similarity. Ph.D. Thesis, Institute of Communicating and Collaborative Systems, Institute of Informatics, University of Edinburgh, 2003.
  • [9] Dean, T.,Wellman, M.: Planning and Control. Morgan Kaufmann, 1991.
  • [10] Dojat, M., Pachet, F., Guessoum, Z., Touchard, D., Harf, A., Brochard, L.: NeoGanesh: A working system for the automated control of assisted ventilation in ICUs. Artificial Intelligence inMedicine, 11, 1997, 97-117.
  • [11] Dojat, M.: Systemes cognitifs pur le traitement d'informations clinques ou biologiques. L'universite Joseph Fourier, Grenoble, France, 1999.
  • [12] Fawcett, T.: An Introduction to ROC Analysis. Pattern Recognition Letters 27(8), 2006, 861-874.
  • [13] Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier, Morgan Kaufmann, CA, 2004.
  • [14] Góra, G.: Domain Knowledge Approximation for Planning and Scheduling. In: Proceedings of CSP'2004, Caputh, Germany, September 24-26, 2004, 241-249.
  • [15] Gruber, T.: Ontology. In: Liu, L., ¨Ozsu, M. T. (eds.): Encyclopedia of Database Systems, Springer, Berlin, 2008.
  • [16] Jankowski, A., Skowron, A.: Wistech Paradigm for Intelligent Systems. LNCS Transactions on Rough Sets VI, LNCS 4374, 2006, 94-132.
  • [17] Nguyen, S., H., Bazan, J., A., Nguyen, H., S., Skowron, A.: Layered Learning for Concept Synthesis, Journal LNCS Transactions on Rough Sets, Heidelberg, Springer-Verlag vol. 1, LNCS 3100, 2004, 193-214.
  • [18] Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht 1991.
  • [19] Pawlak, Z., Skowron, A.: Rudiments of Rough Sets. Information Sciences 177(1), 2007, 3-27; Rough Sets: Some Extensions. Information Sciences 177(1), 2007, 28-40; Rough Sets and Boolean Reasoning. Information Sciences 177(1), 2007, 41-73.
  • [20] Provost, F., Kohavi, R.: On Applied Research in Machine Learning.Machine Learning 30, 1998, 127- 132.
  • [21] The RSES Homepage at logic.mimuw.edu.pl/_rses
  • [22] Schmidt, R., Vorobieva, O., Gierl, L.: Adaptation Problems in Therapeutic Case-Based Reasoning Systems. In: Proceedings of Knowledge-Based Intelligent Information and Engineering Systems, 7th International Conference, (KES 2003), Oxford, UK, September 3-5, 2003, Proceedings, Part I, LNCS 2773, Springer, Heidelberg, 2003, 992-999.
  • [23] Skowron, A., Stepaniuk, J.: InformationGranules and Rough-Neural Computing: Techniques for Computing with Words, Cognitive Technologies Series, Springer-Verlag, Berlin, 2004, 43-84.
  • [24] Staab, S., Studer, R. (eds.): Handbook on Ontologies. Series : International Handbooks on Information Systems, Springer, Berlin, 2004.
  • [25] Stacey, M., McGregor, C.: Temporal Abstraction in Intelligent Clinical Data Analysis: A Survey. Artificial Intelligence in Medicine 39, 2007, 1-24.
  • [26] Swets, J. A.: Measuring the Accuracy of Diagnostic Systems. Science 240, 1988, 1285-1293.
  • [27] Veloso, M., M., Carbonell, J., G.: Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilisation. Machine Learning 10, 1993, 249-278.
  • [28] Wilkins D., E., desJardins M.: A Call for Knowledge-based Planning. AI Magazine, 22(1), 2001, 99-115.
  • [29] http://www.openclinical.org/ontologies.html#definition
  • [30] Yang, Q.: Intelligent Planning. A Decomposition and Abstraction Based Approach. Springer-Verlag, 1997.
  • [31] Zimmerman, T., Kambhampati, K.: Learning-Assisted Automated Planning: Looking Back, Taking Stock, Going Forward. AI Magazine 24(2), 2003, 73-96.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUS5-0016-0011
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.