Skip to main content

Outlier Detection for GP Referrals in Otorhinolaryngology

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12721)

Abstract

Medical referrals come in unstructured text form, and it is a challenge to classify and find outliers among them. While anomaly detection in the text mining domain is not unusual, it is difficult to apply them in public health as it requires precision especially on the medical terms used. This paper proposed the use of ensembled machine learning algorithms to perform clinical text mining on the referrals and find outlying referrals based on control parameters. The result is a set of ICD codes that can be traced back to the relevant referral for the clinician to investigate further.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-77211-6_53
  • Chapter length: 8 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-77211-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. cdc.gov. ICD - ICD-10-CM - International Classification Of Diseases, Tenth Revision, Clinical Modification 2021, 24 January 2021. https://www.cdc.gov/nchs/icd/icd10cm.htm

  2. Gaspar, J., et al.: A systematic review of outliers detection techniques in medical data-preliminary study. In: HEALTHINF (2011)

    Google Scholar 

  3. Yan, K., et al.: A hybrid outlier detection method for health care big data. In: 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom)(BDCloud-SocialCom-SustainCom). IEEE (2016)

    Google Scholar 

  4. Wang, Z., et al.: Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. PLoS One 7(1), e30412 (2012)

    CrossRef  Google Scholar 

  5. Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(suppl_1), D267–D270 (2004)

    Google Scholar 

  6. Amazon Web Services, I. Amazon Comprehend Medical (2021). https://aws.amazon.com/comprehend/medical/

  7. Bhatia, P., et al.: Comprehend medical: a named entity recognition and relationship extraction web service. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE (2019)

    Google Scholar 

  8. McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)

  9. Breunig, M.M., et al. LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (2000)

    Google Scholar 

  10. Chard, K., et al.: A cloud-based approach to medical NLP. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association (2011)

    Google Scholar 

  11. How Amazon Comprehend Medical works - Amazon Comprehend (2021). https://docs.aws.amazon.com/comprehend/latest/dg/how-medical-works.html#:~:text=Amazon%20Comprehend%20Medical%20uses%20a%20pretrained%20natural%20language,conditions,%20medications,%20or%20Protected%20Health%20Information%20%20(PHI

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chee Keong Wee .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Wee, C.K., Wee, N. (2021). Outlier Detection for GP Referrals in Otorhinolaryngology. In: Tucker, A., Henriques Abreu, P., Cardoso, J., Pereira Rodrigues, P., Riaño, D. (eds) Artificial Intelligence in Medicine. AIME 2021. Lecture Notes in Computer Science(), vol 12721. Springer, Cham. https://doi.org/10.1007/978-3-030-77211-6_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-77211-6_53

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77210-9

  • Online ISBN: 978-3-030-77211-6

  • eBook Packages: Computer ScienceComputer Science (R0)