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Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?
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- المؤلفون: Grigull, Lorenz1 ; Mehmecke, Sandra2; Rother, Ann-Katrin3; Blöß, Susanne4; Klemann, Christian5; Schumacher, Ulrike6; Mücke, Urs1; Kortum, Xiaowei7; Lechner, Werner8; Klawonn, Frank7,9
- المصدر:
PLoS ONE. 10/10/2019, Vol. 14 Issue 10, p1-15. 15p.
- الموضوع:
- معلومة اضافية
- نبذة مختصرة :
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 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. [ABSTRACT FROM AUTHOR]
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