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


Autoři: Lorenz Grigull aff001;  Sandra Mehmecke aff002;  Ann-Katrin Rother aff003;  Susanne Blöß aff004;  Christian Klemann aff005;  Ulrike Schumacher aff006;  Urs Mücke aff001;  Xiaowei Kortum aff007;  Werner Lechner aff008;  Frank Klawonn aff007
Působiště autorů: Department of Pediatric Hematology and Oncology, Hannover Medical School, Hannover, Germany aff001;  Nursing Council (Pflegekammer) Lower Saxony, Hannover, Germany aff002;  Department of Pediatrics and Adolescent Medicine, University of Cologne, Cologne, Germany aff003;  Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany aff004;  Department of Pediatric Pneumology, Allergy and Neonatology, Hannover Medical School, Hannover, Germany aff005;  DRK Clementinenkrankenhaus, Hannover, Germany aff006;  Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbuettel, Germany aff007;  Improved Medical Diagnostics IMD GmbH, Donauwoerth, Germany aff008;  Biostatistics, Helmholtz Centre for Infection Research, Braunschweig, Germany aff009
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0222637

Souhrn

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.

Klíčová slova:

Diagnostic medicine – Machine learning – Patient advocacy – Physicians – Psychometrics – Questionnaires – Acromegaly – Fabry disease


Zdroje

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PLOS One


2019 Číslo 10

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