Grammatical Evolutionary Techniques for Prompt Migraine Prediction

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@InProceedings{Pagan:2016:GECCO,
  author =       "Josue Pagan and Jose L. Risco-Martin and 
                 Jose M. Moya and Jose L. Ayala",
  title =        "Grammatical Evolutionary Techniques for Prompt
                 Migraine Prediction",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "973--980",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908897",
  abstract =     "The migraine disease is a chronic headache presenting
                 symptomatic crisis that causes high economic costs to
                 the national health services, and impacts negatively on
                 the quality of life of the patients. Even if some
                 patients can feel unspecific symptoms before the onset
                 of the migraine, these only happen randomly and cannot
                 predict the crisis precisely. In our work, we have
                 proved how migraine crisis can be predicted with high
                 accuracy from the physiological variables of the
                 patients, acquired by a non-intrusive Wireless Body
                 Sensor Network. In this paper, we derive alternative
                 models for migraine prediction using Grammatical
                 Evolution techniques. We obtain prediction horizons
                 around 20 minutes, which are sufficient to advance the
                 drug intake and avoid the symptomatic crisis. The
                 robustness of the models with respect to sensor
                 failures has also been tackled to allow the practical
                 implementation in the ambulatory monitoring platform.
                 The achieved models are non linear mathematical
                 expressions with low computing overhead during the
                 run-time execution in the wearable devices.",
  notes =        "Complutense University of Madrid, Technical University
                 of Madrid

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Pagan Ortiz Josue Jose L Risco-Martin Jose M Moya Jose Luis Ayala Rodrigo

Citations