Universität zu Köln

Grigull, Lorenz, Mehmecke, Sandra, Rother, Ann-Katrin, Bloess, Susanne, Klemann, Christian, Schumacher, Ulrike, Muecke, Urs, Kortum, Xiaowei, Lechner, Werner ORCID: 0000-0003-4255-5269 and Klawonn, Frank ORCID: 0000-0001-9613-182X (2019). Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition? PLoS One, 14 (10). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1932-6203

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Abstract

Background Rare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all individuals affected by (different) RD during the time before diagnosis is established. Objective We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD. Methods 20 interviews with affected individuals with different RD, focusing on the time period before their diagnosis, were performed and qualitatively analyzed. Out of these pre-diagnostic experiences, we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) other common nonrare diseases (NRO) iii.) common chronic diseases (CD), iv.), or psychosomatic/somatoform disorders (PSY). Finally, four combined single machine learning methods and a fusion algorithm were used to distinguish the different answer patterns of the questionnaires. Results The questionnaire contained 53 questions. A total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY. Conclusion Despite the great diversity in presentation and pathogenesis of each RD, patients with RD share surprisingly similar pre-diagnosis experiences. Our questionnaire and data-mining based approach successfully detected unique patterns in groups of individuals affected by a broad range of different rare diseases. Therefore, these results indicate distinct patterns that may be used for diagnostic support in RD.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Grigull, Lorenz UNSPECIFIED UNSPECIFIED UNSPECIFIED Mehmecke, Sandra UNSPECIFIED UNSPECIFIED UNSPECIFIED Rother, Ann-Katrin UNSPECIFIED UNSPECIFIED UNSPECIFIED Bloess, Susanne UNSPECIFIED UNSPECIFIED UNSPECIFIED Klemann, Christian UNSPECIFIED UNSPECIFIED UNSPECIFIED Schumacher, Ulrike UNSPECIFIED UNSPECIFIED UNSPECIFIED Muecke, Urs UNSPECIFIED UNSPECIFIED UNSPECIFIED Kortum, Xiaowei UNSPECIFIED UNSPECIFIED UNSPECIFIED Lechner, Werner UNSPECIFIED orcid.org/0000-0003-4255-5269 UNSPECIFIED Klawonn, Frank UNSPECIFIED orcid.org/0000-0001-9613-182X UNSPECIFIED
URN:	urn:nbn:de:hbz:38-131082
DOI:	10.1371/journal.pone.0222637
Journal or Publication Title:	PLoS One
Volume:	14
Number:	10
Date:	2019
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1932-6203
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language CLINICAL VARIABILITY; MUSCULAR-DYSTROPHY; POMPE-DISEASE; DELAY Multiple languages Multidisciplinary Sciences Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/13108