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On clustering patients with low back pain

van der Hoef, Hanneke

The current study contributes to the search of identifying subgroups of patients with low back pain by using clara: clustering large applications. Different from prior studies, a dimension reduction is provided by selecting key variables found in the literature. In addition, external instead of internal validation criteria are followed. Five groups are identified, which are characterized as: (1) pain has spread down into the legs (2) acute, intense low back pain which is likely to be aggravated by work (3) acute intense low back pain, not aggravated by work, and sleeping problems (4) no (activity) limitations, good recovery rate (5) chronic (i.e. more than 3 months) low back pain with a bad prognosis. Limitations and recommendations are discussed.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000085952/02
Veröffentlicht am 21.02.2019
Zugehörige Institution(en) am KIT Institut für Informationswirtschaft und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Jahr 2019
Sprache Englisch
Identifikator ISSN: 2510-0564
KITopen-ID: 1000091233
Erschienen in Archives of Data Science, Series B (Online First)
Band 1
Heft 1
Seiten B02, 16 S. online
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