Conception, Development and Validation of Classification Methods for Coding Support of Rare Diseases Using Artificial Intelligence.

Journal: Studies in health technology and informatics
Published Date:

Abstract

Automated coding of diseases can support hospitals in the billing of inpatient cases with the health insurance funds. This paper describes the implementation and evaluation of classification methods for two selected Rare Diseases. Different classifiers of an off-the-shelf system and an own application are applied in a supervised learning process and comparatively examined for their suitability and reliability. Using Natural Language Processing and Machine Learning, disease entities are recognized from unstructured historical patient records and new billing cases are coded automatically. The results of the performed classifications show that even with small datasets (≤ 200), high correctness (F1 score ∼0.8) can be achieved in predicting new cases.

Authors

  • Richard Noll
    Institute of Medical Informatics, University Medicine Frankfurt, Goethe University Frankfurt, Frankfurt 60590, Germany.
  • Mirjam Minor
    Department of Informatics, Goethe University Frankfurt, Frankfurt, Germany.
  • Alexandra Berger
    Frankfurt Reference Centre for Rare Diseases, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany.
  • Lukas Naab
    MINDS-Medical GmbH, Frankfurt, Germany.
  • Matthias Bay
    MINDS-Medical GmbH, Frankfurt, Germany.
  • Holger Storf
    Institute of Medical Informatics, University Medicine Frankfurt, Goethe University Frankfurt, Frankfurt 60590, Germany.
  • Jannik Schaaf
    Institute of Medical Informatics, University Medicine Frankfurt, Goethe University Frankfurt, Frankfurt 60590, Germany.