Observational studyPloS one2015
Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications--A Monocentric Observational Pilot Study.
Observational study in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].Schmerz (Berlin, Germany) · 2024Pooled it
- Systematic review of artificial intelligence and digital-automated tools for screening, diagnosis and characterisation of primary ciliary dyskinesia.ERJ open research · 2026Article
- A decision tree-based algorithm for structured risk stratification of rare rheumatic diseases in a tertiary referral setting.Frontiers in medicine · 2026Article
- A labeled medical records corpus for the timely detection of rare diseases using machine learning approaches.Scientific reports · 2025Article
- Article
- Patient-Oriented Questionnaires and Machine Learning for Rare Disease Diagnosis: A Systematic Review.Journal of clinical medicine · 2024Review
- Identification of Smith-Magenis syndrome cases through an experimental evaluation of machine learning methods.Frontiers in computational neuroscience · 2024Article
- [Faster diagnosis of rare diseases with artificial intelligence-A precept of ethics, economy and quality of life].Innere Medizin (Heidelberg, Germany) · 2023Review
- The Impact of Artificial Intelligence in the Odyssey of Rare Diseases.Biomedicines · 2023Review
- A literature review on the analysis of symptom-based clinical pathways: Time for a different approach?PLOS digital health · 2022Article
- Mental health, long-term medication adherence, and the control of asthma symptoms among persons exposed to the WTC 9/11 disaster.The Journal of asthma : official journal of the Association for the Care of Asthma · 2020Article
- Diagnosis of Rare Diseases: a scoping review of clinical decision support systems.Orphanet journal of rare diseases · 2020Article
- Diagnosis support systems for rare diseases: a scoping review.Orphanet journal of rare diseases · 2020Article
- Article
- ERS statement on protracted bacterial bronchitis in children.The European respiratory journal · 2017Review
- Patient's Experience in Pediatric Primary Immunodeficiency Disorders: Computerized Classification of Questionnaires.Frontiers in immunology · 2017Article
- Diagnostic needs for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey.PloS one · 2017Article
- Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial.BMC medical informatics and decision making · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical 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).
objectiveTo develop and test a questionnaire-based and data mining-supported tool providing diagnostic support for selected pulmonary diseases.
methodsFirst, interviews with parents of affected children were conducted and analysed. These parental observations during the pre-diagnostic time formed the basis for a new questionnaire addressing the parents' view on the disease. Secondly, parents with a sick child (e.g. PCD, PBB) answered the questionnaire and a data base was set up. Finally, a computer program consisting of eight different classifiers (support vector machine (SVM), artificial neural network (ANN), fuzzy rule-based, random forest, logistic regression, linear discriminant analysis, naive Bayes and nearest neighbour) and an ensemble classifier was developed and trained to categorise any given new questionnaire and suggest a diagnosis. For estimating the diagnostic accuracy, we applied ten-fold stratified cross validation.
resultsAll questionnaires of patients suffering from CF, asthma (AS), PCD, acute bronchitis (AB) and the healthy control group were correctly diagnosed by the fusion algorithm. For the pneumonia (PM) group 19/21 (90.5%) and for the PBB group 17/18 (94.4%) correct diagnoses could be reached. The program detected the correct diagnoses with an overall sensitivity of 98.8%. Receiver operating characteristics (ROC) analyses confirmed the accuracy of this diagnostic tool. Case studies highlighted the applicability of the tool in the daily work of a GP.
conclusionFor children with symptoms of pulmonary diseases a questionnaire-based diagnostic support tool using data mining techniques exhibited good results in arriving at diagnostic suggestions. In the hands of a doctor, this tool could be of value in arousing awareness for rare pulmonary diseases such as PCD or CF.
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