Automated Triaging Medical Referral for Otorhinolaryngology Using Data Mining and Machine Learning Techniques

Abstract

Public hospitals receive and triage a large volume of medical referrals for otorhinolaryngology annually and it can be a challenge to derive knowledge from them as they are written in unstructured text and may be unavailable in electronic formats. Acquiring knowledge and insights from these referrals are important to public health management and policymakers. Triaging of general practitioner (GP) referrals for ear, nose, and throat (ENT) specialists is a manual process performed by experienced clinicians, but it is time-consuming. This paper proposes utilising machine learning and data mining to automate the process of referrals. In this study, an ensemble of machine learning algorithms to perform clinical text mining against the unstructured referral text in order to derive the relationship among the discovered medical terms was proposed and implemented. A set of comprehensive term sets' association rules which describe the entire referral dataset's characteristics was obtained from the association rule mining experiments. The neural network-based text classification model that can classify referrals with high accuracy was developed, tested and reported in this paper.