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.
|
Statistics for this ePrint Item |
Actions (login required)
 |
Archive Repository Staff Only |