Abstract
The degree of fulfillment of clinical guidelines is considered a key factor when evaluating the quality of a clinical service. Guidelines can be seen as processes describing the sequence of activities to be done. Consequently, workflow formalisms seem to be a valid approach to model the flow of actions in the guideline and their temporal aspects. The application of a guideline to a specific patient (guideline instance) can be modeled by means of a workflow case. The best (worst) application of a guideline, represented as a reference workflow case, can be used to evaluate the quality of the service, by comparing the optimal case with specific patient instances. On the other hand, the correct application of a guideline to a patient involves the fulfillment of the guideline temporal constraints. Thus, the evaluation of the temporal similarity degree between different workflow cases is a key aspect in evaluating health care quality. In this work, we represent a portion of the stroke guideline using a temporal workflow schema and we propose a method to evaluate the temporal similarity between workflow cases. Our proposal, based on temporal constraint networks, consists of a linear combination of functions to differentiate intra-task and inter-task temporal distances.
This work was partially supported by the Spanish MEC under the FPU national plan (grant ref. AP2003-4476) and the national projects TIC2003-09400-C04 / TIN2006-15460-C04-01.
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References
Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26, 832–843 (1983)
Chittaro, L., Combi, C.: Visualizing queries on databases of temporal histories: new metaphors and their evaluation. Data Knowl. Eng. 44(2), 239–264 (2003)
Dojat, M., Ramaux, N., Fontaine, D.: Scenario recognition for temporal reasoning in medical domains. Artificial Intelligence in Medicine 14(1-2), 139–155 (1998)
Freksa, C.: Temporal reasoning based on semi-intervals. Artificial Intelligence 54(1), 199–227 (1992)
Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3), 263–286 (2001)
Mannila, H., Moen, P.: Similarity between event types in sequences. In: DaWaK 1999: Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery, London, UK, pp. 271–280. Springer, Heidelberg (1999)
Panzarasa, S., Stefanelli, M.: Workflow management systems for guideline implementation. Neurological Sciences 27 (2006)
The Stroke Prevention and Educational Awareness Diffusion (SPREAD) Collaboration. The italian guidelines for stroke prevention. Neurological Sciences, 21 (2000)
Quaglini, S., Ciccarese, P.: Models for guideline representation. Neurological Sciences 27 (2006)
Shahar, Y., Musen, M.A.: Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine 8(3), 267–298 (1996)
Vilain, M., Kautz, H.: Constraint propagation algorithms for temporal reasoning. In: Proceedings of the National Conference on Artificial Intelligence (AAAI 1986), USA, vol. 6, pp. 132–144 (1986)
Yi, B.-K., Roh, J.-W.: Similarity search for interval time sequences. In: DASFAA, vol. 2973, pp. 232–243 (2004)
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Combi, C., Gozzi, M., Juarez, J.M., Marin, R., Oliboni, B. (2007). Querying Clinical Workflows by Temporal Similarity . In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_63
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DOI: https://doi.org/10.1007/978-3-540-73599-1_63
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