Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?

• Published in: PloS one Vol. 14; no. 10; p. e0222637
• Main Authors: Grigull, Lorenz, Mehmecke, Sandra, Rother, Ann-Katrin, Blöß, Susanne, Klemann, Christian, Schumacher, Ulrike, Mücke, Urs, Kortum, Xiaowei, Lechner, Werner, Klawonn, Frank
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 10.10.2019; Public Library of Science (PLoS)
• Subjects: Adolescent; Adult; Algorithms; Amyotrophic lateral sclerosis; Artificial Intelligence; Biology and Life Sciences; Care and treatment; Chronic Disease - epidemiology; Chronic diseases; Computer and Information Sciences; Computer science; Data Mining; Diagnosis; Diagnostic software; Diagnostic systems; Female; Health care access; Health Personnel - statistics & numerical data; Health Status; Hematology; Humans; Learning algorithms; Machine Learning; Male; Medical diagnosis; Medical personnel; Medical schools; Medicine and Health Sciences; Mining industry; Oncology; Pathogenesis; Patients; Pattern recognition; Pattern recognition systems; Pediatrics; People and Places; Physicians; Practice; Questionnaires; Rare diseases; Rare Diseases - classification; Rare Diseases - diagnosis; Rare Diseases - epidemiology; Research and Analysis Methods; Respiratory distress syndrome; Social Sciences; Somatoform disorders; Surveys and Questionnaires; Teaching methods; Young Adult; Germany
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0222637

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. We aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD. 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. 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. 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.