RT Journal Article
SR Electronic
T1 Early dietitian referral in lung cancer: use of machine learning
JF BMJ Supportive & Palliative Care
JO BMJ Support Palliat Care
FD British Medical Journal Publishing Group
SP 56
OP 59
DO 10.1136/bmjspcare-2021-003487
VO 14
IS 1
A1 Chung, Michael
A1 Phillips, Iain
A1 Allan, Lindsey
A1 Westran, Naomi
A1 Hug, Adele
A1 Evans, Philip M
YR 2024
UL http://spcare.bmj.com/content/14/1/56.abstract
AB Objectives The Dietetic Assessment and Intervention in Lung Cancer (DAIL) study was an observational cohort study. It triaged the need for dietetic input in patients with lung cancer, using questionnaires with 137 responses. This substudy tested if machine learning could predict need to see a dietitian (NTSD) using 5 or 10 measures.Methods 76 cases from DAIL were included (Royal Surrey NHS Foundation Trust; RSH: 56, Frimley Park Hospital; FPH 20). Univariate analysis was used to find the strongest correlates with NTSD and ‘critical need to see a dietitian’ CNTSD. Those with a Spearman correlation above ±0.4 were selected to train a support vector machine (SVM) to predict NTSD and CNTSD. The 10 and 5 best correlates were evaluated.Results 18 and 13 measures had a correlation above ±0.4 for NTSD and CNTSD, respectively, producing SVMs with 3% and 7% misclassification error. 10 measures yielded errors of 7% (NTSD) and 9% (CNTSD). 5 measures yielded between 7% and 11% errors. SVM trained on the RSH data and tested on the FPH data resulted in errors of 20%.Conclusions Machine learning can predict NTSD producing misclassification errors <10%. With further work, this methodology allows integrated early referral to a dietitian independently of a healthcare professional.