ArticlePloS one2019
Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].Schmerz (Berlin, Germany) · 2024Pooled it
- Development, Implementation, and Preliminary Results of a Novel Social Network for Rare Disease Communities: Mixed Methods Study.JMIR human factors · 2026Article
- Health care, social, educational, and employment needs of individuals affected by rare diseases and their families in Catalonia, Spain.Orphanet journal of rare diseases · 2026Article
- Effectiveness of cognitive training on Lamb-Shaffer syndrome: a case report.Dementia & neuropsychologia · 2026Article
- A decision tree-based algorithm for structured risk stratification of rare rheumatic diseases in a tertiary referral setting.Frontiers in medicine · 2026Article
- Article
- Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review.Journal of clinical medicine · 2024Review
- A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D.Orphanet journal of rare diseases · 2023Article
- Article
- Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey.Journal of medical Internet research · 2021Article
- Article
- Development of a Social Network for People Without a Diagnosis (RarePairs): Evaluation Study.Journal of medical Internet research · 2020Article
- The Odyssey from Symptom to Diagnosis of Pulmonary Hypertension from the Patients and Spouses Perspective.Journal of primary care & community healthArticle
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRare 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.
objectiveWe aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.
methods20 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.
resultsThe 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.
conclusionDespite 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.
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