PT  - JOURNAL ARTICLE
AU  - Chung, Michael
AU  - Phillips, Iain
AU  - Allan, Lindsey
AU  - Westran, Naomi
AU  - Hug, Adele
AU  - Evans, Philip M
TI  - Early dietitian referral in lung cancer: use of machine learning
AID  - 10.1136/bmjspcare-2021-003487
DP  - 2024 Mar 01
TA  - BMJ Supportive & Palliative Care
PG  - 56--59
VI  - 14
IP  - 1
4099  - http://spcare.bmj.com/content/14/1/56.short
4100  - http://spcare.bmj.com/content/14/1/56.full
SO  - BMJ Support Palliat Care2024 Mar 01; 14
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.