Klinische Padiatrie · 2019
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СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Sieg Anna-Lena, Martin Das Anibh, Maria Muschol Nicole, Köhn Anja, Lampe Christina, Kortum Xiauwei, Mehmecke Sandra, Blöß Susanne, Lechner Werner, Klawonn Frank, Grigull Lorenz
Аннотация
BACKGROUND: Diagnosing a rare metabolic disease challenges physicians and affected individuals and their families. To support the diagnostic pathway, a diagnostic tool was developed using the experiences of the affected individuals gained in interviews.
METHODS: 17 interviews with parents or individuals with a selected rare metabolic disease (Mucopolysaccharidosis (MPS), M. Fabry and M. Gaucher) were performed and qualitatively analysed using the standardized methods of Colaizzi. The results are reflected in diagnostic questionnaires. The questionnaires were distributed and answered by parents or individuals with an established diagnosis of MPS, M. Fabry or M. Gaucher and a control group. Four combined data mining classifiers were trained to detect suspicious answer patterns in the questionnaires.
RESULTS: 56 questionnaires were used for training and cross-validation tests of the binary data mining system resulting in a sensitivity value of 91% for the diagnosis 'MPS'. Another 20 questionnaires which have not been used for the training process could be evaluated as a preliminary prospective test. Out of these 20 questionnaires the test delivered 18 correct diagnoses (90%).
DISCUSSION AND CONCLUSIONS: Questionnaires for diagnostic support based on interviews with parents and affected individuals were developed and answer patterns were analysed with an ensemble of classifiers. Although preliminary, the results illustrate the potential of answer pattern recognition using data mining techniques. This approach might prove useful for diagnostic support in selected metabolic diseases.
METHODS: 17 interviews with parents or individuals with a selected rare metabolic disease (Mucopolysaccharidosis (MPS), M. Fabry and M. Gaucher) were performed and qualitatively analysed using the standardized methods of Colaizzi. The results are reflected in diagnostic questionnaires. The questionnaires were distributed and answered by parents or individuals with an established diagnosis of MPS, M. Fabry or M. Gaucher and a control group. Four combined data mining classifiers were trained to detect suspicious answer patterns in the questionnaires.
RESULTS: 56 questionnaires were used for training and cross-validation tests of the binary data mining system resulting in a sensitivity value of 91% for the diagnosis 'MPS'. Another 20 questionnaires which have not been used for the training process could be evaluated as a preliminary prospective test. Out of these 20 questionnaires the test delivered 18 correct diagnoses (90%).
DISCUSSION AND CONCLUSIONS: Questionnaires for diagnostic support based on interviews with parents and affected individuals were developed and answer patterns were analysed with an ensemble of classifiers. Although preliminary, the results illustrate the potential of answer pattern recognition using data mining techniques. This approach might prove useful for diagnostic support in selected metabolic diseases.