J W Moor1, V Paleri2, J Edwards3. 1. ENT Department, Leeds Teaching Hospitals NHS Trust, UK. 2. ENT Department, Royal Marsden Hospital, London, UK. 3. Temporal Computing Ltd, Department of Computer Science, Newcastle upon Tyne, UK.
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.
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.
Entities:
Keywords:
Diagnostic Techniques And Procedures; Head And Neck Neoplasms; Machine Learning
Authors: George N Okoli; Otto L T Lam; Viraj K Reddy; Leslie Copstein; Nicole Askin; Anubha Prashad; Jennifer Stiff; Satya Rashi Khare; Robyn Leonard; Wasifa Zarin; Andrea C Tricco; Ahmed M Abou-Setta Journal: BMJ Open Date: 2021-11-09 Impact factor: 2.692
Authors: Vinidh Paleri; John Hardman; Theofano Tikka; Paula Bradley; Paul Pracy; Cyrus Kerawala Journal: Head Neck Date: 2020-05-11 Impact factor: 3.147