Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications--A Monocentric Observational Pilot Study.

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)

Heat map analysis of the AS and PBB group for all 45 questions (Q1–Q45).

<p>The heat map analysis of this pair indicates that one question was more supportive in differentiating between AS and PBB patients than the others: no. 31: ‘Are there any allergies detected in your child?’.</p

Heat map analysis of the CF and PCD group for all 45 questions (Q1–Q45).

<p>The questions no. 27: ‘Do you think that your child often suffers from an inflammation of the middle ear?’, no. 38: ‘Do you think infections in your child never clear up completely, but affect the ears first and then continue by affecting the lungs?’ were the most helpful questions for distinguishing between CF and PCD in the heat map analysis.</p

List of Questions with Highest Information Gain for the Computer.

<p>List of Questions with Highest Information Gain for the Computer.</p

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

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 nonrare 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

ROC curves for patients with PBB.

<p>Area under the curve (AUC).</p

Heat map analysis of the PCD and PBB group for all 45 questions (Q1–Q45).

<p>The comparison of answer patterns between PCD and PBB parents showed that the following three questions were particularly helpful in distinguishing the two diseases. no. 26: ‘Would you say that your child is suffering from cough since birth?’ no. 34: ‘Would you say that your child’s running nose is not influenced by the season?’ no. 38: ‘Would you say that infections in your child never clear up completely, but affect the ears first and then continue by affecting the lungs?’.</p

ROC curves for patients with AS.

<p>Area under the curve (AUC).</p

Diagnostic needs for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey.

BACKGROUND: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. METHODS:A two-step Delphi survey was conducted using web-based technologies in all centers for rare diseases in Germany. Moreover, the leading patient support group, the German foundation for rare diseases (ACHSE), was contacted to involve patients as experts in their disease. In the survey the experts were invited to name rare diseases with special need for diagnostic improvement. Secondly, communal experiences of affected individuals were collected. RESULTS:166 of 474 contacted experts (35%) participated in the first round of the Delphi process and 95 of 166 (57%) participated in the second round. Metabolic (n = 74) and autoimmune diseases (n = 39) were ranked the highest for need for diagnostic support. For three diseases (i.e. scleroderma, Pompe's disease, and pulmonary arterial hypertension), a crucial need for diagnostic support was explicitly stated. A typical experience of individuals with a rare disease was stigmatization of having psychological or psychosomatic problems. In addition, most experts endured an 'odyssey' of seeing many different medical specialists before a correct diagnosis (n = 38) was confirmed. CONCLUSION:There is need for improving the diagnostic process in individuals with rare diseases. Shared experiences in individuals with a rare disease were observed, which could possibly be utilized for diagnostic support in the future

Rare diseases with need for diagnostic support (2nd Delphi round).

<p>Rare diseases with need for diagnostic support (2nd Delphi round).</p