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Towards knowledge-driven symptom monitoring & trigger detection of primary headache disorders

Mathias De Brouwer (UGent) , Nicolas Vandenbussche (UGent) , Bram Steenwinckel (UGent) , Marija Stojchevska (UGent) , Jonas Van Der Donckt (UGent) , Vic Degraeve, Filip De Turck (UGent) , Koen Paemeleire (UGent) , Sofie Van Hoecke (UGent) and Femke Ongenae (UGent)
(2022) COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION. p.264-268
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Abstract
Headache disorders are experienced by many people around the world. In current clinical practice, the follow-up and diagnosis of headache disorder patients only happens intermittently, based on subjective data self-reported by the patient. The mBrain system tries to make this process more continuous, autonomous and objective by additionally collecting contextual and physiological data via a wearable, mobile app and machine learning algorithms. To support the monitoring of headache symptoms during attacks for headache classification and the detection of headache triggers, much knowledge and contextual data is available from heterogeneous sources, which can be consolidated with semantics. This paper presents a demonstrator of knowledge-driven services that perform these tasks using Semantic Web technologies. These services are deployed in a distributed cascading architecture that includes DIVIDE to derive and manage the RDF stream processing queries that perform the contextually relevant filtering in an intelligent and efficient way.
Keywords
symptom monitoring, headache trigger detection, headache classification, SemanticWeb, RDF Stream Processing, knowledge-driven

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MLA
De Brouwer, Mathias, et al. “Towards Knowledge-Driven Symptom Monitoring & Trigger Detection of Primary Headache Disorders.” COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION, edited by Frédérique Laforest et al., ACM, 2022, pp. 264–68, doi:10.1145/3487553.3524256.
APA
De Brouwer, M., Vandenbussche, N., Steenwinckel, B., Stojchevska, M., Van Der Donckt, J., Degraeve, V., … Ongenae, F. (2022). Towards knowledge-driven symptom monitoring & trigger detection of primary headache disorders. In F. Laforest, R. Troncy, L. Médini, & I. Herman (Eds.), COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION (pp. 264–268). https://doi.org/10.1145/3487553.3524256
Chicago author-date
De Brouwer, Mathias, Nicolas Vandenbussche, Bram Steenwinckel, Marija Stojchevska, Jonas Van Der Donckt, Vic Degraeve, Filip De Turck, Koen Paemeleire, Sofie Van Hoecke, and Femke Ongenae. 2022. “Towards Knowledge-Driven Symptom Monitoring & Trigger Detection of Primary Headache Disorders.” In COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION, edited by Frédérique Laforest, Raphaël Troncy, Lionel Médini, and Ivan Herman, 264–68. New York, NY, USA: ACM. https://doi.org/10.1145/3487553.3524256.
Chicago author-date (all authors)
De Brouwer, Mathias, Nicolas Vandenbussche, Bram Steenwinckel, Marija Stojchevska, Jonas Van Der Donckt, Vic Degraeve, Filip De Turck, Koen Paemeleire, Sofie Van Hoecke, and Femke Ongenae. 2022. “Towards Knowledge-Driven Symptom Monitoring & Trigger Detection of Primary Headache Disorders.” In COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION, ed by. Frédérique Laforest, Raphaël Troncy, Lionel Médini, and Ivan Herman, 264–268. New York, NY, USA: ACM. doi:10.1145/3487553.3524256.
Vancouver
1.
De Brouwer M, Vandenbussche N, Steenwinckel B, Stojchevska M, Van Der Donckt J, Degraeve V, et al. Towards knowledge-driven symptom monitoring & trigger detection of primary headache disorders. In: Laforest F, Troncy R, Médini L, Herman I, editors. COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION. New York, NY, USA: ACM; 2022. p. 264–8.
IEEE
[1]
M. De Brouwer et al., “Towards knowledge-driven symptom monitoring & trigger detection of primary headache disorders,” in COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION, Lyon, France (online), 2022, pp. 264–268.
@inproceedings{01H3C66B6GAMFFNPDWY51T5SBQ,
  abstract     = {{Headache disorders are experienced by many people around the world. In current clinical practice, the follow-up and diagnosis of headache disorder patients only happens intermittently, based on subjective data self-reported by the patient. The mBrain system tries to make this process more continuous, autonomous and objective by additionally collecting contextual and physiological data via a wearable, mobile app and machine learning algorithms. To support the monitoring of headache symptoms during attacks for headache classification and the detection of headache triggers, much knowledge and contextual data is available from heterogeneous sources, which can be consolidated with semantics. This paper presents a demonstrator of knowledge-driven services that perform these tasks using Semantic Web technologies. These services are deployed in a distributed cascading architecture that includes DIVIDE to derive and manage the RDF stream processing queries that perform the contextually relevant filtering in an intelligent and efficient way.}},
  author       = {{De Brouwer, Mathias and Vandenbussche, Nicolas and Steenwinckel, Bram and Stojchevska, Marija and Van Der Donckt, Jonas and Degraeve, Vic and De Turck, Filip and Paemeleire, Koen and Van Hoecke, Sofie and Ongenae, Femke}},
  booktitle    = {{COMPANION PROCEEDINGS OF THE WEB CONFERENCE 2022, WWW 2022 COMPANION}},
  editor       = {{Laforest, Frédérique and Troncy, Raphaël and Médini, Lionel and Herman, Ivan}},
  isbn         = {{9781450391306}},
  keywords     = {{symptom monitoring,headache trigger detection,headache classification,SemanticWeb,RDF Stream Processing,knowledge-driven}},
  language     = {{eng}},
  location     = {{Lyon, France (online)}},
  pages        = {{264--268}},
  publisher    = {{ACM}},
  title        = {{Towards knowledge-driven symptom monitoring & trigger detection of primary headache disorders}},
  url          = {{http://doi.org/10.1145/3487553.3524256}},
  year         = {{2022}},
}
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