Early Diagnosis Service for Latent Patients of Incurable Diseases

  • Yoko Nishihara
  • Yoshimune Hiratsuka
  • Akira Murakami
  • Toshiro Kumakawa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6797)


It is considered that many people are struggling with diseases that are difficult to cure. In general, it takes a long time to diagnose and to cure such diseases. In Japan, methods for curing 56 incurable diseases have been studied. However, methods for early diagnosis of incurable diseases have not been studied. To make early diagnoses for incurable diseases improves the quality of life of patients. This paper proposes a new service system that supports latent patients of incurable diseases by early diagnosis. For using this system, users have to prepare a text in which episodes about patients’ experiences that have been caused because of their diseases are written. The system takes such a text as input, and then the system extracts common factors among episodes, i.e., keywords appearing in several episodes. The system output the extracted keywords as keywords relating to symptoms of an incurable disease. We experimented the system and extracted keywords relating to the symptoms of retinitis pigmentosa which was one of the incurable eye diseases. Most of the symptoms relating to the extracted keywords were not known even by medical doctors. Some of them indicated symptoms in the early stage of the disease. The experiment brought us one step closer to the early diagnosis of incurable disease as one of the service systems.


service system of early diagnosis incurable diseases keyword relating to a symptom of incurable disease 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lozano, A.M., Kalia, S.K.: New Movement in Parkinson’s. Scientific American 293, 68–75 (2005)CrossRefGoogle Scholar
  2. 2.
    Japan Incurable Diseases Information Center,
  3. 3.
  4. 4.
    Yamanishi, K., Takeuchi, J., Williamas, G., Milne, P.: On-line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms. Data Mining and Knowledge Discovery Journal 8(3), 275–300 (2004)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ohsawa, Y., Benson, N.E., Yachida, M.: KeyGraph: Automatic Indexing by Co-occurrence Graph based on Building Construction Metaphor. In: Proceedings of the Advances in Digital Libraries Conference, pp. 12–18 (1998)Google Scholar
  6. 6.
    National Center for Biotechnology Information,
  7. 7.
    Karni, R., Kaner, M.: An engineering tool for the conceptual design of service systems. In: Spath, Fahnrich (eds.) Advances in Service Innovations. Springer, NY (2006)Google Scholar
  8. 8.
    Cohn, J.N., Finkelstein, S., McVeigh, G., Morgan, D., LeMay, L., Robinson, J., Mock, J.: Noninvasive Pulse Wave Analysis for the Early Detection of Vascular Disease. Hypertension 26, 503–508 (1995)CrossRefGoogle Scholar
  9. 9.
    Farrington, C.P., Andrews, N.J., Beale, A.D., Catchpole, M.A.: A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease. Journal of the Royal Statistical Society 159(3), 547–563 (1996)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yoko Nishihara
    • 1
  • Yoshimune Hiratsuka
    • 2
  • Akira Murakami
    • 3
  • Toshiro Kumakawa
    • 2
  1. 1.Department of Systems Innovation, School of EngineeringThe University of TokyoBunkyoJapan
  2. 2.Department of Management ScienceNational Institute of Public HealthWakoJapan
  3. 3.Department of OphthalmologyJuntendo University, School of MedicineBunkyoJapan

Personalised recommendations