BMJ supportive & palliative care · 2024
Early dietitian referral in lung cancer: use of machine learning.
Наблюдательное исследованиеСтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chung Michael, Phillips Iain, Allan Lindsey, Westran Naomi, Hug Adele, Evans Philip M
Аннотация
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.
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.