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

      Grigull, Lorenz; Mehmecke, Sandra; Rother, Ann-Katrin; Blöß, Susanne; Klemann, Christian; Schumacher, Ulrike; Mücke, Urs; Kortum, Xiaowei; Lechner, Werner; Klawonn, Frank; et al. (Public Library of Science (PLoS), 2019-10-10)
      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.
    • Diagnostic needs for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey.

      Blöß, Susanne; Klemann, Christian; Rother, Ann-Katrin; Mehmecke, Sandra; Schumacher, Ulrike; Mücke, Urs; Mücke, Martin; Stieber, Christiane; Klawonn, Frank; Kortum, Xiaowei; et al. (2017)
      Worldwide approximately 7,000 rare diseases have been identified. Accordingly, 4 million individuals live with a rare disease in Germany. The mean time to diagnosis is about 6 years and patients receive several incorrect diagnoses during this time. A multiplicity of factors renders diagnosing a rare disease extremely difficult. Detection of shared phenomena among individuals with different rare diseases could assist the diagnostic process. In order to explore the demand for diagnostic support and to obtain the commonalities among patients, a nationwide Delphi survey of centers for rare diseases and patient groups was conducted.
    • Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial.

      Grigull, Lorenz; Lechner, Werner; Petri, Susanne; Kollewe, Katja; Dengler, Reinhard; Mehmecke, Sandra; Schumacher, Ulrike; Lücke, Thomas; Schneider-Gold, Christiane; Köhler, Cornelia; et al. (2016)
      Diagnosis of neuromuscular diseases in primary care is often challenging. Rare diseases such as Pompe disease are easily overlooked by the general practitioner. We therefore aimed to develop a diagnostic support tool using patient-oriented questions and combined data mining algorithms recognizing answer patterns in individuals with selected neuromuscular diseases. A multicenter prospective study for the proof of concept was conducted thereafter.
    • Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications--A Monocentric Observational Pilot Study.

      Rother, Ann-Katrin; Schwerk, Nicolaus; Brinkmann, Folke; Klawonn, Frank; Lechner, Werner; Grigull, Lorenz; Helmholtz Centre for infection research, Inhoffenstr. 7, 38124 Braunschweig, Germany. (2015)
      Clinical symptoms in children with pulmonary diseases are frequently non-specific. Rare diseases such as primary ciliary dyskinesia (PCD), cystic fibrosis (CF) or protracted bacterial bronchitis (PBB) can be easily missed at the general practitioner (GP).
    • Improving the Decision Support in Diagnostic Systems using Classifier Probability Calibration

      Kortum, Xiaowei; Grigull, Lorenz; Lechner, Werner; Klawonn, Frank; HZI,Helmholtz-Zentrum für Infektionsforschung GmbH, Inhoffenstr. 7,38124 Braunschweig, Germany. (Springer, 2018-11-09)
      In modern medical diagnoses, classifying a patient’s disease is often realized with the help of a system-aided symptoms interpreter. Most of these systems rely on supervised learning algorithms, which can statistically extend the doctor’s logic capabilities for interpreting and examining symptoms, thus supporting the doctor to find the correct diagnosis. Besides, these algorithms compute classifier scores and class labels that are used to statistically characterize the system’s confidence level on a patient’s type of disease. Unfortunately, most classifier scores are based on an arbitrary scale but not uniformed, thus the interpretations often lack of clinical significance and evaluation criterion. Especially combining multiple classifier scores within a diagnostic system, it is essential to apply a calibration process to make the different scores comparable. As a frequently used calibration technique, we adapted isotonic regression for our medical diagnostic support system, to provide a flexible and effective scaling process that consequently calibrates the arbitrary scales of classifiers’ scores. In a comparative evaluation, we show that our disease diagnostic system with isotonic regression can actively improve the diagnostic result based on an ensemble of classifiers, also effectively remove outliers from data, thus optimize the decision support system to obtain better diagnostic results.
    • Künstliche Intelligenz zur diagnostischen Unterstützung ausgewählter seltener lysosomaler Speichererkrankungen: Ergebnisse einer Pilotstudie.

      Sieg, Anna-Lena; Anibh, Martin; Muschol, Nicole Maria; Köhn, Anja; Lampe, Christina; Kortum, Xiauwei; Mehmecke, Sandra; Blöß, Susanne; Lechner, Werner; Klawonn, Frank; et al. (Thieme, 2019-02-10)
      Hintergrund: Die Diagnosestellung einer seltenen Stoffwechselerkrankung stellt eine Herausforderung für Familien und betreuende Ärzte dar. Um den Weg zur Diagnose zu unterstützen, wurde ein diagnostisches Werkzeug entwickelt, welches die Erfahrungen Betroffener nutzt.