Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
MetadataShow full item record
AbstractBACKGROUND: 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 non-rare 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.
CitationPLoS One. 2019 Oct 10;14(10):e0222637. doi: 10.1371/journal.pone.0222637. eCollection 2019.
AffiliationHZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany.
PublisherPublic Library of Science (PLoS)
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International
- Künstliche Intelligenz zur diagnostischen Unterstützung ausgewählter seltener lysosomaler Speichererkrankungen: Ergebnisse einer Pilotstudie.
- Authors: Sieg AL, Martin Das A, Maria Muschol N, Köhn A, Lampe C, Kortum X, Mehmecke S, Blöß S, Lechner W, Klawonn F, Grigull L
- Issue date: 2019 Mar
- Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial.
- Authors: Grigull L, Lechner W, Petri S, Kollewe K, Dengler R, Mehmecke S, Schumacher U, Lücke T, Schneider-Gold C, Köhler C, Güttsches AK, Kortum X, Klawonn F
- Issue date: 2016 Mar 8
- Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications--A Monocentric Observational Pilot Study.
- Authors: Rother AK, Schwerk N, Brinkmann F, Klawonn F, Lechner W, Grigull L
- Issue date: 2015
- Health-related quality of life among adults with diverse rare disorders.
- Authors: Bogart KR, Irvin VL
- Issue date: 2017 Dec 7
- Development of a Social Network for People Without a Diagnosis (RarePairs): Evaluation Study.
- Authors: Kühnle L, Mücke U, Lechner WM, Klawonn F, Grigull L
- Issue date: 2020 Sep 29