Background: In the primary and secondary medical health sector, patient reported outcome measures (PROMs) are widely used to assess a patient's disease-related functional health state. However, the World Health Organization (WHO), in its recently adopted resolution on "strengthening rehabilitation in all health systems", encourages that all health sectors, not only the rehabilitation sector, classify a patient's functioning and health state according to the International Classification of Functioning, Disability and Health (ICF).
Aim: This research sought to optimize machine learning (ML) methods that fully and automatically link information collected from PROMs in persons with unspecific chronic low back pain (cLBP) to limitations in activities and restrictions in participation that are listed in the WHO core set categories for LBP. The study also aimed to identify the minimal set of PROMs necessary for linking without compromising performance.
Methods: A total of 806 patients with cLBP completed a comprehensive set of validated PROMs and were interviewed by clinical psychologists who assessed patients' performance in activity limitations and restrictions in participation according to the ICF brief core set for low back pain (LBP). The information collected was then utilized to further develop random forest (RF) methods that classified the presence or absence of a problem within each of the activity participation ICF categories of the ICF core set for LBP. Further analyses identified those PROM items relevant to the linking process and validated the respective linking performance that utilized a minimal subset of items.
Results: Compared to a recently developed ML linking method, receiver operating characteristic curve (ROC-AUC) values for the novel RF methods showed overall improved performance, with AUC values ranging from 0.73 for the ICF category d850 to 0.81 for the ICF category d540. Variable importance measurements revealed that minimal subsets of either 24 or 15 important PROM variables (out of 80 items included in full set of PROMs) would show similar linking performance.
Conclusions: Findings suggest that our optimized ML based methods more accurately predict the presence or absence of limitations and restrictions listed in ICF core categories for cLBP. In addition, this accurate performance would not suffer if the list of PROM items was reduced to a minimum of 15 out of 80 items assessed.
Keywords: international classification of functioning; low back pain; machine learning; patient reported outcome measures; random forest.