Abstract
Medical diagnostic procedures generally comprise a step of collecting patients’ symptoms, a step of diagnostic decisions, and a step of selecting appropriate methods of treatment. In traditional medical treatment based on analogical inference, analyzing present collected symptoms and choosing symptoms to query are mightily important for the diagnosis and these are essential conditions for appropriate treatment. Use of information systems that support present diversity of symptoms information and considerable options for the next step can avoid missing out timely and useful knowledge during the procedures. We have developed an application that having user interfaces guiding various analytic cases and their next optional choice and clinicians are able to improve the efficiency of procedures with this. By analyzing semantically linked data to symptoms, the application is possible to support efficiently collecting symptoms and selecting methods of treatment. This interfaces help users by requiring a minimal operation but supporting diverse probabilities.
Chapter PDF
Similar content being viewed by others
References
Ledley, R.S., Lusted, L.B.: Reasoning foundations of medical diagnosis; symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science 130(3366), 9–21 (1959)
Wikipedia, Medical diagnosis, http://en.wikipedia.org/wiki/Medical_diagnosis
Warner, H.R., Toronto, A.F., Veasey, L.G., Stephenson, R.: A mathematical approach to medical diagnosis: application to congenital heart disease. MD Comput. 177(3), 177–183 (1961)
Szolovits, P., Pauker, S.G.: Categorical and probabilistic reasoning in medical diagnosis. Artif. Intell. 11, 115–144 (1978)
Amaral, M.B., Satomura, Y., Honda, M., Sato, T.: A psychiatric diagnostic system integrating probabilistic and categorical reasoning. Methods Inf. Med. 34(3), 232–243 (1995)
Adlassnig, K.: Fuzzy Set Theory in Medical Diagnosis. IEEE T. Syst. Man Cyb. 16(2), 260–265 (1986)
Kononenko, I.: Inductive and Bayesian Learning in Medical Diagnosis. Appl. Artif. Intell. 7(4), 317–337 (1993)
Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109 (2001)
Begg, C.B., Greenes, R.A.: Assessment of Diagnostic Tests When Disease Verification is Subject to Selection Bias. Biometrics 39(1), 207–215 (1983)
McNeil, B.J., Keeler, E., Adelstein, J.: Primer on Certain Elements of Medical Decision Making. New Engl. J. Med. 293, 211–215 (1975)
Wang, X., Qu, H., Liu, P., Cheng, Y.: A self-learning expert system for diagnosis in traditional Chinese medicine. Extert. Syst. Appl. 26(4), 557–566 (2004)
Hogeboom, C.J., Sherman, K.J., Cherkin, D.C.: Variation in diagnosis and treatment of chronic low back pain by traditional Chinese medicine acupuncturists. Complement Ther. Med. 9(3), 154–166 (2001)
Jang, H., Kim, J., Kim, S.-K., Kim, C., Bae, S.-H., Kim, A., Eum, D.-M., Song, M.-Y.: Ontology for Medicinal Materials Based on Traditional Korean Medicine. Bioinformatics 26(18), 2359–2360 (2010)
Klyne, G., Carroll, J.: Resource Description Framework (RDF): Concepts and Abstract Syntax. W3C Recommendation (2004)
McGuinness, D., Harmelen, F.: OWL Web Ontology Language Overview. W3C Recommendation (2004)
Tim, B.-L.: Design Issues: Linked Data (2006), http://www.w3.org/DesignIssues/LinkedData.html
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jang, H., Oh, YT., Kim, A., Kim, S.K. (2013). User Guiding Information Supporting Application for Clinical Procedure in Traditional Medicine. In: Yamamoto, S. (eds) Human Interface and the Management of Information. Information and Interaction for Health, Safety, Mobility and Complex Environments. HIMI 2013. Lecture Notes in Computer Science, vol 8017. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39215-3_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-39215-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39214-6
Online ISBN: 978-3-642-39215-3
eBook Packages: Computer ScienceComputer Science (R0)