RESUME

RÉSUMÉ: A Temporal-Abstraction System for Patient Monitoring. data. The temporal-abstraction task is crucial for planning treatment, for executing treatment plans, for identifying clinical problems, and for revising treatment plans. The RÉSUMÉ system is based on a model of three basic temporal-abstraction mechanisms: point temporal abstraction, a mechanism for abstracting the values of several parameters into a value of another parameter; temporal inference, a mechanism for inferring sound logical conclusions over a single interval or two meeting intervals; and temporal interpolation, a mechanism for bridging nonmeeting temporal intervals. Making explicit the knowledge required for temporal abstraction supports the acquisition and the sharing of that knowledge. We have implemented the RESUME system using the CLIPS knowledge-representation shell. The RÉSUMÉ system emphasizes the need for explicit representation of temporal-abstraction knowledge, and the advantages of modular, task-specific but domain-independent architectures for building medical knowledge-based systems.


References in zbMATH (referenced in 14 articles , 1 standard article )

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  1. Teijeiro, T.; Félix, P.: On the adoption of abductive reasoning for time series interpretation (2018)
  2. Campos, M.; Juárez, J. M.; Palma, J.; Marín, R.: Using temporal constraints for temporal abstraction (2010) ioport
  3. Bernshtein, L. S.; Kovalev, S. M.; Muravskii, A. V.: Models of representation of fuzzy temporal knowledge in databases of temporal series (2009)
  4. Otero, A.; Félix, P.; Barro, S.: A fuzzy constraint satisfaction approach for signal abstraction (2009)
  5. Montani, Stefania: Exploring new roles for case-based reasoning in heterogeneous AI systems for medical decision support. (2008) ioport
  6. Spokoiny, Alex; Shahar, Yuval: Incremental application of knowledge to continuously arriving time-oriented data (2008) ioport
  7. Xiong, Ning; Funk, Peter: Concise case indexing of time series in health care by means of key sequence discovery. (2008) ioport
  8. Sacchi, Lucia; Larizza, Cristiana; Combi, Carlo; Bellazzi, Riccardo: Data mining with Temporal Abstractions: Learning rules from time series (2007) ioport
  9. Spokoiny, Alex; Shahar, Yuval: An active database architecture for knowledge-based incremental abstraction of complex concepts from continuously arriving time-oriented raw data (2007) ioport
  10. Swift, Stephen; Kok, Joost; Liu, Xiaohui: Learning short multivariate time series models through evolutionary and sparse matrix computation (2006)
  11. Kiseliova, Tatiana; Wagner, Hubert: A generalized time quantifier approach to approximate reasoning (2004)
  12. Tawfik, Ahmed Y.: Changing times: A causal theory of probabilistic temporal reasoning. (1999)
  13. Shahar, Y.; Molina, M.: Knowledge-based spatiotemporal linear abstraction (1998)
  14. Shahar, Yuval: A framework for knowledge-based temporal abstraction (1997)