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Sensemaking in Intelligent Health Data Analytics
RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab. KTH Royal Institute of Technology, Sweden.ORCID iD: 0000-0001-7949-1815
RISE, Swedish ICT, SICS.
2015 (English)In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 29, no 2, p. 143-152Article in journal (Refereed) Published
Abstract [en]

A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.

Place, publisher, year, edition, pages
2015, 6. Vol. 29, no 2, p. 143-152
Keywords [en]
Artificial intelligence, Massive data, Health data, Intelligent data analytics, Syndromic surveillance, Sensemaking
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-24403DOI: 10.1007/s13218-015-0349-0OAI: oai:DiVA.org:ri-24403DiVA, id: diva2:1043484
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2019-07-09Bibliographically approved

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Boman, Magnus

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