Diagnosis Code Assignment Support Using Random Indexing of Patient Records – A Qualitative Feasibility Study

  • Aron Henriksson
  • Martin Hassel
  • Maria Kvist
Conference paper

DOI: 10.1007/978-3-642-22218-4_45

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6747)
Cite this paper as:
Henriksson A., Hassel M., Kvist M. (2011) Diagnosis Code Assignment Support Using Random Indexing of Patient Records – A Qualitative Feasibility Study. In: Peleg M., Lavrač N., Combi C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science, vol 6747. Springer, Berlin, Heidelberg

Abstract

The prediction of diagnosis codes is typically based on free-text entries in clinical documents. Previous attempts to tackle this problem range from strictly rule-based systems to utilizing various classification algorithms, resulting in varying degrees of success. A novel approach is to build a word space model based on a corpus of coded patient records, associating co-occurrences of words and ICD-10 codes. Random Indexing is a computationally efficient implementation of the word space model and may prove an effective means of providing support for the assignment of diagnosis codes. The method is here qualitatively evaluated for its feasibility by a physician on clinical records from two Swedish clinics. The assigned codes were in this initial experiment found among the top 10 generated suggestions in 20% of the cases, but a partial match in 77% demonstrates the potential of the method.

Keywords

ICD-10 Assignment Random Indexing Electronic Patient Records Qualitative Evaluation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aron Henriksson
    • 1
  • Martin Hassel
    • 1
  • Maria Kvist
    • 1
    • 2
  1. 1.Department of Computer and System Sciences (DSV)Stockholm UniversityKistaSweden
  2. 2.Department of Clinical Immunology and Transfusion MedicineKarolinska University HospitalStockholmSweden

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