Abstract
In this paper, we describe an approach to utilize Case-Based Reasoning (CBR) methods for trend prognoses for medical problems. Since using conventional methods for reasoning over time does not fit for course predictions without medical knowledge of typical course pattern, we have developed abstraction methods suitable for integration into our Case-Based Reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. We apply them to the monitoring of the kidney function in an Intensive Care Unit (ICU) setting. We generate course-characteristic trend descriptions of the renal function over the course of time. Using Case-Based Reasoning retrieval methods, we search in the case base for courses similar to the, current trend descriptions. We present a current course together with similar courses as comparisons and as possible prognoses to the user.
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References
Wenkebach, U., Pollwein, B., Finsterer, U.: Visualization of large datasets in intensive care. In: Proc Annu Symp Comput Appl Med Care (1992) 18–22
Allen, J.P.: Towards a general theory of action and time. Artificial Intelligence 23, (1984) 123–154
Keravnou, E.T.: Modelling Medical Concepts as Time Objects. In: P. Barahona, M. Stefanelli, J. Wyatt (eds.): Artificial Intelligence in Medicine, Lecture Notes in Artificial Intelligence 934, Springer-Verlag, Berlin Heidelberg New York (1995) 67–78
Robeson, S.M., Steyn, D.G.: Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations. Atmospheric Environment 24 B 2, (1990)
Shahar, Y., Musen, M.A.: RESUME: A Temporal-Abstraction System for Patient Monitoring. Computers and Biomedical Research 26 (1993) 255–273
Haimowitz, I.J., Kohane, I.S.: Automated Trend Detection with Alternate Temporal Hypotheses. In: Bajcsy, R. (ed.): Proceedings of IJCAI-93, Morgan Kaufmann, San Mateo, CA (1993) 146–151
Miksch, S., Horn, W., Popow, C., Paky, F.: Therapy Planning Using Qualitative Trend Descriptions. Barahona, P., Stefanelli, M., Wyatt, J. (Eds.) Artificial Intelligence in Medicine, Lecture Notes in Artificial Intelligence 934, Springer-Verlag, Berlin Heidelberg New York (1995) 209–217
Aamodt, A.: Case-Based Reasoning: Foundation Issues. Methodological Variation-and System Approaches, AlCOM 7 (1994) 39–59
Tversky, A.: Features of Similarity. Psychological Review 84 (1977) 327–352
Anderson, J.R.: A theory of the origins of human knowledge. Artificial Intelligence 40, Special Volume on Machine Learning (1989) 313–351
Smyth, B., Keane, M.T.: Retrieving Adaptable Cases: The Role of Adaptation Knowledge in Case Retrieval. First European Workshop on Case-Based Reasoning, EWCBR-93, (1993) 76–81
DeSarbo, W.S., Johnson, M.D., Manrei, A.K., Manrai, L.A., Edwards, E.A.: TSCALE: A new multidemensional scaling procedure based on Tversky’s contrast model. Psychometrika 57 (1992) 43–69
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Schmidt, R., Gierl, L. (2000). Prognoses for Multiparametric Time Courses. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_5
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DOI: https://doi.org/10.1007/3-540-39949-6_5
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