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A simple method for heuristic modeling of expert knowledge in chronic disease: identification of prognostic subgroups in rheumatology
Linköping University, Department of Behavioural Sciences and Learning, Cognition, Development and Disability. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Medical and Health Sciences, Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Public Health Sciences, Centre for Public Health Sciences.ORCID iD: 0000-0001-6049-5402
Linköping University, Department of Medical and Health Sciences, Health Technology Assessment. Linköping University, Faculty of Health Sciences.
Linköping University, Department of Clinical and Experimental Medicine, Rheumatology. Linköping University, Faculty of Health Sciences.ORCID iD: 0000-0002-0153-9249
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2008 (English)In: eHealth Beyond the Horizon – Get IT There, IOS Press, 2008, Vol. 136, 157-162 p.Conference paper, Published paper (Refereed)
Abstract [en]

Identification of prognostic subgroups is of key clinical interest at the early stages of chronic disease. The aim of this study is to examine whether representation of physicians' expert knowledge in a simple heuristic model can improve data mining methods in prognostic assessments of patients with rheumatoid arthritis (RA). Five rheumatology consultants' experiences of clinical data patterns among RA patients, as distinguished from healthy reference populations, were formally represented in a simple heuristic model. The model was used in K-mean-clustering to determine prognostic subgroups. Cross-sectional validation using physician's global assessment scores indicated that the simple heuristic model performed better than crude data made in identification of prognostic subgroups of RA patients. A simple heuristic model of experts' knowledge was found useful for semi-automatic data mining in the chronic disease setting. Further studies using categorical baseline data and prospective outcome variables are warranted and will be examined in the Swedish TIRA-program.

Place, publisher, year, edition, pages
IOS Press, 2008. Vol. 136, 157-162 p.
Series
Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365 ; Vol. 136
Keyword [en]
Knowledge engineering, Clinical Decision Support Systems, Semiautomated Data Mining, Rheumatoid Arthritis, Mathematical models in medicine
National Category
Social Work
Identifiers
URN: urn:nbn:se:liu:diva-18106ISI: 000274308700026PubMedID: 18487724ISBN: 978-1-58603-864-9 (print)ISBN: 978-1-60750-333-0 (print)OAI: oai:DiVA.org:liu-18106DiVA: diva2:214757
Conference
21st International Congress of the European-Federation-for-Medical-Informatic (MIE2008), Gothenburg, Sweden, MAY 25-28, 2008
Available from: 2009-05-06 Created: 2009-05-06 Last updated: 2017-04-11Bibliographically approved
In thesis
1. Focus on Chronic Disease through Different Lenses of Expertise: Towards Implementation of Patient-Focused Decision Support Preventing Disability: The Example of Early Rheumatoid Arthritis
Open this publication in new window or tab >>Focus on Chronic Disease through Different Lenses of Expertise: Towards Implementation of Patient-Focused Decision Support Preventing Disability: The Example of Early Rheumatoid Arthritis
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Fokus på expertis inom kronisk sjukdom : Implementering av prognostiskt beslutsstöd med exempel från reumatoid artrit
Abstract [en]

Introduction: Rheumatoid arthritis (RA) is a chronic inflammatory disease. Treatment strategies emphasize early multi-professional interventions to reduce disease activity and to prevent disability, but there is a lack of knowledge on how optimal treatment can be provided to each individual patient.

Aim: To elucidate how clinical manifestations of early RA are associated to disease and disability outcomes, to strive for greater potential to establish prognosis in early RA, and to facilitate implementation of decision support through analyses of the decision-making environment in chronic care.

Methods: Multivariate statistics and mathematical modelling, as well as field observations and focus group interviews.

Results: Decision support: A prognostic tree that predicted patients with a poor prognosis (moderate or high levels of DAS-28) at one year after diagnosis had a performance of 25% sensitivity, 90% specificity and a positive predictive value of 76%. Implementation of a decision support application at a rheumatology unit should include taking into account incentive structures, workflow and awareness, as well as informal communication structures. Prognosis: A considerable part of the variance in disease activity at one year after diagnosis could be explained by disease progression during the first three months after diagnosis. Using different types of knowledge – different expertise – prior to standardized data mining methods was found to be a promising when mining (clinical) data for new patterns that elicit new knowledge. Disease and disability: Women report more fatigue than men in early RA, although the difference is not consistently significant. Fatigue in early RA is closely and rather consistently related to disease activity, pain and activity limitation, as well as to mental health and sleep disturbance.

Conclusion: A decision tree was designed to identify patients at risk of poor prognosis at one year after the diagnosis of RA. When constructing prediction rules for good or poor prognosis, including more measures of disease and disability progressions showed promise. Using different types of knowledge – different lenses of expertise – prior to standardized data mining methods was also a promising method when mining (clinical) data for new patterns that elicit new knowledge.

Abstract [en]

Introduktion: Reumatoid artrit (RA) är en kronisk inflammatorisk sjukdom. Dagens behandlingsstrategi bygger på tidiga multiprofessionella insatser för att reducera sjukdomsaktivitet och minska risken för framtida funktionshinder. Idag finns stora datamängder tillgängliga gällande medicinering och utfall vid RA. Dessa data erbjuder möjligheter att generera ny kunskap som kan användas för att forma beslutsstöd.

Syfte: Att undersöka hur olika kliniska manifestationer vid tidig RA samvarierar med funktionshinder och sjukdomsaktivitet, att pröva metoder att ställa prognos vid tidig RA, och att analysera en kontext för beslutsfattande inom vård av kroniskt sjuka.

Metod: Multivariat statistik och matematisk modellering, samt observationsstudier och fokusgruppsintervjuer.

Resultat: Beslutsstöd: Ett beslutsträd utformades för att bestämma vilka patienter som har dålig prognos (måttlig eller hög DAS-28) ett år efter diagnos. Beslutsträdet hade 25 % sensitivitet, 90 % specificitet och ett positivt prediktivt värde på 76 %. Vid införande av beslutsstöd på en reumatologisk klinik befanns det nödvändigt att hänsyn tas till incitamentsstrukturer, arbetsflöde och samarbetsformer. Informella kommunikationsstrukturer kan också ha stort inflytande på klinisk praxis. Prognos: En betydande del av variansen i sjukdomsaktivitet ett år efter diagnos kan förklaras av sjukdomsprogression första tre månaderna efter diagnos. Att formalisera olika experters erfarenheter före standardiserade ”data mining” metoder är en lovande ansats när man letar efter mönster i (kliniska) databaser. Funktionshinder och sjukdomsaktivitet: Kvinnor rapporterar mer trötthet än män vid tidig RA, men skillnaden är inte konsistent över tid. Trötthet vid tidig RA är nära relaterat till sjukdomsaktivitet, smärta och aktivitets begränsningar, men också till mental hälsa och sömnstörningar.

Slutsats: Ett beslutsträd har utformats för att predicera patienter med dålig prognos inom tidig RA. Studier av fler mått på sjukdoms- och funktionshindersprogression behövs vid konstruktion av prediktionsregler för god eller dålig prognos framledes. Att använda sig av kunskap från olika experter – olika experters glasögon – vid sökandet efter mönster i stora datamängder för att generera ny kunskap är en lovande metodik. Implementering av beslutsstöd bör göras under övervägande av incitamentsstrukturer, arbetsflöde och samarbetsformer.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 126 p.
Series
Linköping Studies in Arts and Science, ISSN 0282-9800 ; 481Studies from the Swedish Institute for Disability Research, ISSN 1650-1128 ; 29
Keyword
Clinical Decision Support, Rheumatology, Prognosis, Disability, Fatigue, Knowledge Engineering, Kliniskt beslutsstöd, reumatologi, prognos, funktionshinder, trötthet, kunskapsmodellering
National Category
Social Work
Identifiers
urn:nbn:se:liu:diva-18112 (URN)978-91-7393-613-2 (ISBN)
Public defence
2009-05-29, Key 1, Hus Key, Campus Valla, Linköpings universitet, Linköping, 13:00 (Swedish)
Opponent
Supervisors
Available from: 2009-05-06 Created: 2009-05-06 Last updated: 2014-09-25Bibliographically approved

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Dahlström, ÖrjanTimpka, ToomasHass, UrsulaSkogh, ThomasThyberg, Ingrid

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