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Patient classification of two-week wait referrals for suspected head and neck cancer: a machine learning approach

Published online by Cambridge University Press:  02 September 2019

J W Moor ,
V Paleri  and
J Edwards
J W Moor*
Affiliation:
ENT Department, Leeds Teaching Hospitals NHS Trust, UK
V Paleri
Affiliation:
ENT Department, Royal Marsden Hospital, London, UK
J Edwards
Affiliation:
Temporal Computing Ltd, Department of Computer Science, Newcastle upon Tyne, UK
*
Author for correspondence: Mr James Moor, ENT Dept, Leeds General Infirmary, Leeds LS1 3EX, UK E-mail: [email protected]
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Abstract

Background

Machine learning algorithms could potentially be used to classify patients referred on the two-week wait pathway for suspected head and neck cancer. Patients could be classified into ‘predicted cancer’ or ‘predicted non-cancer’ groups.

Methods

A variety of machine learning algorithms were assessed using the clinical data of 5082 patients. These patients had previously been referred via the two-week wait pathway for suspected head and neck cancer to two separate tertiary referral centres in the UK. Outcomes from machine learning classification were analysed in comparison to known clinical diagnoses.

Results

Variational logistic regression was the most clinically useful technique of those chosen to perform the analysis and patient classification; the proportion of patients correctly classified as having ‘non-cancer’ was 25.8 per cent, with a false negative rate of 1 out of 1000.

Conclusion

Machine learning algorithms can accurately and effectively classify patients referred with suspected head and neck cancer symptoms.

Keywords

Machine LearningHead And Neck NeoplasmsDiagnostic Techniques And Procedures
Type
Main Articles
Information
The Journal of Laryngology & Otology , Volume 133 , Issue 10 , October 2019 , pp. 875 - 878
Copyright
Copyright © JLO (1984) Limited, 2019 

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Footnotes

Mr J W Moor takes responsibility for the integrity of the content of the paper

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