Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires
Ann-Katrin Rother1, Nicolaus Schwerk2, Folke Brinkmann3
1Department of Paediatric Haematology and Oncology, University Children's Hospital, Hanover Medical School, Hanover, Germany.
Insights
A new data mining tool aids GPs in diagnosing pediatric pulmonary diseases like primary ciliary dyskinesia (PCD) and cystic fibrosis (CF) using parent questionnaires. This diagnostic support system achieved high accuracy, improving early detection of rare conditions.
Area of Science:
- Pediatric Pulmonology
- Medical Informatics
- Data Mining Applications
Background:
- Pediatric pulmonary disease symptoms are often non-specific, leading to missed diagnoses of rare conditions like primary ciliary dyskinesia (PCD), cystic fibrosis (CF), and protracted bacterial bronchitis (PBB) by general practitioners (GPs).
- Early and accurate diagnosis is crucial for effective management and improved patient outcomes.
Purpose of the Study:
- To develop and validate a novel diagnostic support tool for pediatric pulmonary diseases.
- The tool integrates parental observations via a questionnaire with data mining techniques for enhanced diagnostic accuracy.
Main Methods:
- Parental interviews informed the creation of a disease-specific questionnaire.
- A database was established from questionnaires completed by parents of children with various pulmonary conditions.
- Multiple machine learning classifiers and an ensemble model were trained and evaluated using ten-fold cross-validation.
Main Results:
- The diagnostic tool achieved high accuracy across multiple conditions, including perfect diagnosis for cystic fibrosis (CF), asthma (AS), primary ciliary dyskinesia (PCD), and acute bronchitis (AB).
- High diagnostic rates were observed for pneumonia (90.5%) and protracted bacterial bronchitis (94.4%).
- The ensemble classifier demonstrated an overall sensitivity of 98.8%, with ROC analyses confirming its diagnostic accuracy.
Conclusions:
- A questionnaire-based diagnostic support tool leveraging data mining shows significant promise for assisting GPs in identifying pediatric pulmonary diseases.
- This tool can enhance physician awareness of rare conditions such as PCD and CF, facilitating timely intervention.
- The system's applicability in daily general practice was highlighted through case studies.
Background:
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).
Objective:
To develop and test a questionnaire-based and data mining-supported tool providing diagnostic support for selected pulmonary diseases.
Methods:
First, 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.
Results:
All 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.
Conclusion:
For 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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