Hostname: page-component-5db58dd55d-m58mf Total loading time: 0 Render date: 2026-05-30T19:42:30.637Z Has data issue: false hasContentIssue false
  • English
  • Français

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: jamesmoor@nhs.net
Get access Rights & Permissions [Opens in a new window]

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

Information

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

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

References

1MacKay, DJC. Information Theory, Inference, and Learning Algorithms. Cambridge: Cambridge University Press, 2003Google Scholar
2Clifton, DA. Machine Learning for Healthcare Technologies. London: Institution of Technology and Engineering, 2016Google Scholar
3Oxford Cancer Intelligence Unit. Profile of Head and Neck Cancers in England: Incidence, Mortality and Survival. In: http://www.ncin.org.uk/cancer_type_and_topic_specific_work/cancer_type_specific_work/head_and_neck_cancers/head_and_neck_cancer_hub/resources [5 October 2016]Google Scholar
4NHS England. Achieving World Class Cancer Outcomes: Taking the strategy forward. In: https://www.england.nhs.uk/wp-content/uploads/2016/05/cancer-strategy.pdf [5 October 2016]Google Scholar
5NHS England Interim Management and Support. Delivering Cancer Waiting Times: A Good Practical Guide. In: https://www.england.nhs.uk/wp-content/uploads/2015/03/delivering-cancer-wait-times.pdf [5 October 2016]Google Scholar
6Drinnan, M, Paleri, V, Kumar, R, Mehanna, H. Efficacy of the two week wait referral system for head and neck cancer: a systematic review. Ann R Coll Surg Engl 2012;94:101–5Google Scholar
7Statistics vs. Machine Learning, fight! In: http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/ [17 November 2016]Google Scholar
8Breiman, L. Statistical modeling: the two cultures. Statistical Science 2001;16:199–215Google Scholar
9Tikka, T, Pracy, P, Paleri, V. Refining the head and neck cancer referral guidelines: a two-centre analysis of 4715 referrals. Clin Otolaryngol 2016;41:66–75Google Scholar
10Webb, AR, Copsey, AD. Statistical Pattern Recognition. Chichester: Wiley, 2011Google Scholar
11Scikit-learn. In: http://scikit-learn.org/stable/index.html [17 November 2016]Google Scholar
12Seone, J, Takkouche, B, Varela-Centelles, P, Tomas, I, Seoane-Romero, JM. Impact of delay in diagnosis on survival to head and neck carcinomas: a systematic review with meta-analysis. Clin Otolaryngol 2012;37:99–106Google Scholar
13Hippersley-Cox, J, Coupland, C, Robson, J, Brindle, P. Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database. BMJ 2010;341:c6624Google Scholar
14QCancer. In: https://qcancer.org [17 November 2016]Google Scholar
15Cancer Decision Support (CDS) tool. In: http://www.macmillan.org.uk/about-us/health-professionals/programmes-and-services/prevention-early-diagnosis-programme/cancer-decision-support-tool.html [17 November 2016]Google Scholar
16Taylor, RA, Pare, JR, Venkatesh, AK, Mowafi, H, Melnick, ER, Fleischman, W et al. Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med 2016;23:269–78Google Scholar
17Gultepe, E, Green, JP, Nguyen, H, Adams, J, Albertson, T, Tagkopoulos, I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc 2014;21:315–25Google Scholar
18Klann, JG, Anand, V, Downs, SM. Patient-tailored prioritization for a pediatric care decision support system through machine learning. J Am Med Inform Assoc 2013;20:e267–74Google Scholar