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Sensemaking in Intelligent Data Analytics
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0001-7949-1815
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0001-5620-6305
2015 (English)In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987Article in journal (Refereed) Epub ahead of print
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.
Keyword [en]
Artificial intelligence Massive data Health data Intelligent data analytics Syndromic surveillance Sensemaking
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-158906DOI: 10.1007/s13218-015-0349-0OAI: oai:DiVA.org:kth-158906DiVA: diva2:779932
Note

QP 2015

Available from: 2015-01-13 Created: 2015-01-13 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Health Data: Representation and (In)visibility
Open this publication in new window or tab >>Health Data: Representation and (In)visibility
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Health data requires context to be understood. I show how, by examining two areas: self-surveillance, with a focus on representation of bodily data, and mass-surveillance, with a focus on representing populations. I critically explore how Information and Communication Technology (ICT) can be made to represent individuals and populations, and identify implications of such representations. My contributions are: (i) the design of a self-tracking stress management system, (ii) the design of a mass-surveillance system based on mobile phone data, (iii) an empirical study exploring how users of a fitness tracker make sense of their generated data, (iv) an analysis of the discourse of designers of a syndrome surveillance system, (v) a critical analysis of the design process of a mass-surveillance system, and (vi) an analysis of the historicity of the concepts and decisions taken during the design of a stress management system. I show that producing health data, and subsequently the technological characteristics of algorithms that produce them depend on factors present in the ICT design process. These factors determine how data is made to represent individuals and populations in ways that may selectively make invisible parts of the population, determinants of health, or individual conception of self and wellbeing. In addition, I show that the work of producing data does not stop with the work of the engineers who produce ICT-based systems: maintenance is constantly required.

Abstract [sv]

För att förstå hälsodata krävs sammanhang. Jag visar hur detta kan erhållas, genom två fallstudier: en om självövervakning, med fokus på representation av kroppsdata, samt en om massövervakning, med fokus på representation av populationer. Jag granskar kritiskt hur informationsteknologi (IT) kan fås att representera såväl individer som populationer och vilka följder det får. Mina bidrag är: (i) utformningen av ett självövervakningssystem för stresshantering, (ii) utformningen av ett massövervakningssystem baserat på data från mobiltelefonanvändning, (iii) en empirisk studie av hur användare av en hälsosensor begriper det data som sensorn genererar, (iv) en diskursiv analys av hur syndromövervakningssystem utformas, (v) en kritisk analys av processer kring att utforma ett massövervakningssystem, samt (vi) en analys av den historiska korrektheten i begrepp och beslutsfattande i samband med utformningen av ett stresshanteringssystem. Jag visar att produktion av hälsodata, liksom tekniska beskrivningar av de algoritmer som används i den processen, beror av faktorer som hänger samman med IT-utformningsprocessen. Dessa faktorer avgör sedan hur data kan fås att representera individer och populationer på sätt som kan rendera delar av en population, hälsodeterminanter, eller individens självuppfattning och förståelse av välmående osynliga. Jag visar också att arbetet med att producera data inte är avslutat i och med det ingenjörsarbete som krävs för att IT-systemen ska byggas: konstant underhåll krävs också.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. 61 p.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 14:17
Series
SICS Dissertation Series, ISSN 1101-1335 ; 72
National Category
Computer Science
Identifiers
urn:nbn:se:kth:diva-158909 (URN)978-91-7595-403-5 (ISBN)
Public defence
2015-01-29, Sal B, Electrum, KTH-ICT, Isafordsgatan 16, Kista, 14:00 (English)
Opponent
Supervisors
Note

QC 20150114

Available from: 2015-01-14 Created: 2015-01-13 Last updated: 2015-03-03Bibliographically approved

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