Abstract
A diagnosis system assists medical doctors to figure out a disease that causes the symptoms of a sick patient. Standardized questionnaires are one way to gather patients’ subjective perceptions about their present and also past healthy conditions. Such questionnaires often cover several question fields with the focus of significant symptoms as suitable disease indicators. Each question provides a different association strength with a disease group. And in most cases, a combination of question/answer pattern is needed to provide strong evidence for a particular disease. Nevertheless, people would like to keep the number of questions to be answered at a minimum for the patient. In this study, we address this aim and introduce an algorithm that reduces the size of a questionnaire by dynamically selecting patient-oriented questions to provide strong evidence for or against a suspected diagnosis tendency. It reduces the number of question to the most relevant ones. We evaluate our self-adapting questioning algorithm with 354 patients from a rare disease questionnaire survey.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Auria, L., Moro, R.A.: Support vector machines (SVM) as a technique for solvency analysis (2008)
Grigull, L., Lechner, W., Petri, S., Kollewe, K., Dengler, R., Mehmecke, S., Schumacher, U., Lücke, T., Schneider-Gold, C., Köhler, C., et al.: Diagnostic support for selected neuromuscular diseases using answer-pattern recognition and data mining techniques: a proof of concept multicenter prospective trial. BMC Med. Inform. Decis. Mak. 16(1), 31 (2016)
Huyn, N., Melmon, K., Perrone, A.: Computerized clinical questionnaire with dynamically presented questions. US Patent Ap. 09/910,463, 20 July 2001
Jansen, A.C., van Aalst-Cohen, E.S., Hutten, B.A., Büller, H.R., Kastelein, J.J., Prins, M.H.: Guidelines were developed for data collection from medical records for use in retrospective analyses. J. Clin. Epidemiol. 58(3), 269–274 (2005)
Kortum, X., Grigull, L., Muecke, U., Lechner, W., Klawonn, F.: Diagnosis support for orphan diseases: a case study using a classifier fusion method. In: Yin, H., Gao, Y., Li, B., Zhang, D., Yang, M., Li, Y., Klawonn, F., Tallón-Ballesteros, A.J. (eds.) IDEAL 2016. LNCS, vol. 9937, pp. 379–385. Springer, Cham (2016). doi:10.1007/978-3-319-46257-8_41
Liaw, A., Wiener, M.: Classification and regression by random forest. R News 2(3), 18–22 (2002)
Ma, L., Liu, X., Song, L., Zhou, C., Zhao, X., Zhao, Y.: A new classifier fusion method based on historical and on-line classification reliability for recognizing common CT imaging signs of lung diseases. Comput. Med. Imaging Graph. 40, 39–48 (2015)
Madabhushi, A., Agner, S., Basavanhally, A., Doyle, S., Lee, G.: Computer-aided prognosis: predicting patient and disease outcome via quantitative fusion of multi-scale, multi-modal data. Comput. Med. Imaging Graph. 35(7), 506–514 (2011)
Pohar, M., Blas, M., Turk, S.: Comparison of logistic regression and linear discriminant analysis: a simulation study. Metodoloski zvezki 1(1), 143 (2004)
Sboner, A., Eccher, C., Blanzieri, E., Bauer, P., Cristofolini, M., Zumiani, G., Forti, S.: A multiple classifier system for early melanoma diagnosis. Artif. Intell. Med. 27(1), 29–44 (2003)
Zhang, X., Klawonn, F., Grigull, L., Lechner, W.: VoQs: a web application for visualization of questionnaire surveys. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds.) IDA 2015. LNCS, vol. 9385, pp. 334–343. Springer, Cham (2015). doi:10.1007/978-3-319-24465-5_29
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Kortum, X., Grigull, L., Lechner, W., Klawonn, F. (2017). A Dynamic Adaptive Questionnaire for Improved Disease Diagnostics. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-68765-0_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68764-3
Online ISBN: 978-3-319-68765-0
eBook Packages: Computer ScienceComputer Science (R0)