Outlier detection for GP referrals in otorhinolaryngology

Wee, Chee Keong and Wee, Nathan (2021) Outlier detection for GP referrals in otorhinolaryngology. In: 19th International Conference on Artificial Intelligence in Medicine (AIME 2021), 15 June - 18 June 2021, Porto, Portugal.


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.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Virtual conference.
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 10 Mar 2022 04:36
Last Modified: 17 Mar 2022 00:04
Uncontrolled Keywords: medical referrals
Fields of Research (2008): 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460201 Artificial life and complex adaptive systems
42 HEALTH SCIENCES > 4299 Other health sciences > 429999 Other health sciences not elsewhere classified
46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460208 Natural language processing
Identification Number or DOI: https://doi.org/10.1007/978-3-030-77211-6_53
URI: http://eprints.usq.edu.au/id/eprint/46985

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