Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jun 15, 2020
Date Accepted: Oct 5, 2020
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Patient triage by topic modelling of referral letters: Feasibility study
ABSTRACT
Background:
Musculoskeletal conditions are managed within primary care with referral to secondary care when a specialist opinion is required. The ever increasing demand of healthcare resources emphasizes the need for new triage methods to streamline care pathways with the ultimate aim of ensuring that patients receive timely and optimal care. Information contained in referral letters underpins the referral decision-making process but is yet to be explored systematically for the purposes of treatment prioritization for musculoskeletal conditions.
Objective:
This study aims to explore the feasibility of using natural language processing and machine learning to automate triage of patients with musculoskeletal conditions by analyzing information from referral letters.
Methods:
We used latent Dirichlet allocation to model each referral letter as a finite mixture over an underlying set of topics and model each topic as an infinite mixture over an underlying set of topic probabilities. The topic model was evaluated in the context of automating patient triage. Given a set of treatment outcomes, a binary classifier was trained for each outcome using previously extracted topics as the input features of the machine learning algorithm. In addition, qualitative evaluation was performed to assess human interpretability of topics.
Results:
The prediction accuracy of binary classifiers outperformed the stratified random classifier by a large margin giving an indication that topic modelling could be used to support patient triage. Qualitative evaluation confirmed high clinical interpretability of the topic model.
Conclusions:
The results established the feasibility of using natural language processing and machine learning to automate triage of patients with knee and/or hip pain by analyzing information from their referral letters.
Citation
