An implicit approach to deal with periodically repeated medical data

  • Authors:

  • Bela Stantic;Paolo Terenziani;Guido Governatori;Alessio Bottrighi;Abdul Sattar

  • Affiliations:

  • Institute for Integrated and Intelligent Systems, Griffith University, 170 Kessels Road Nathan, Queensland 4111, Australia;Dipartimento di Informatica, Universita' del Piemonte Orientale, viale Teresa Michel 11, 15121 Alessandria, Italy;National Information and Communications Technology, P.O. Box 6020, St. Lucia, Queensland 4067, Australia;Dipartimento di Informatica, Universita' del Piemonte Orientale, viale Teresa Michel 11, 15121 Alessandria, Italy;Institute for Integrated and Intelligent Systems, Griffith University, 170 Kessels Road Nathan, Queensland 4111, Australia and National Information and Communications Technology, P.O. Box 6020, St ...

  • Venue:
  • Artificial Intelligence in Medicine
  • Year:
  • 2012

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Abstract

Context: Temporal information plays a crucial role in medicine, so that in medical informatics there is an increasing awareness that suitable database approaches are needed to store and support it. Specifically, a great amount of clinical data (e.g., therapeutic data) are periodically repeated. Although an explicit treatment is possible in most cases, it causes severe storage and disk I/O problems. Objective: In this paper, we propose an innovative approach to cope with periodic relational medical data in an implicit way. Methods: We propose a new data model, representing periodic data in a compact (implicit) way, which is a consistent extension of TSQL2 consensus approach. Then, we identify some important types of temporal queries, and present query answering algorithms to answer them. Finally, we also run experiments to evaluate our approach. Results: The experiments show that our approach outperforms current explicit approaches, especially as regard disk I/O. Conclusion: We have provided an implicit approach to periodic data with is a consistent extension of TSQL2 (and which is thus grant interoperable with it), and we have experimentally proven that it outperforms current explicit approaches.