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Mapping patient reported outcome measures for low back pain to the International Classification of Functioning, Disability and Health using random forests.

BACKGROUND AND AIM
This proof-of-concept study demonstrates and validates a novel approach that automatically translates information derived from patient reported outcome measures (PROMs) into a standardized functioning report based on the International Classification of Functioning, Disability and Health (ICF).


METHODS
The translation algorithm was developed using data from a total of 244 patients with chronic low back pain (cLBP) who completed the Roland-Morris disability questionnaire as well as the Pain Disability Index, and who were interviewed using the activity and participation component of the ICF brief core set for low back pain. Random forest machine learning models that predict the presence or absence of an impairment at the specific ICF category based on the information extracted from the PROMs as well as the personal factors age and gender were developed and validated. An additional external data set from 19 patients with moderate to severe cLBP was used to avoid overfitting and to prove the generalizability of the fitted prediction models.


RESULTS
Accuracy of the prediction models was found to be acceptable for the most relevant ICF brief core set categories for low back pain if applied at the population level.


DISCUSSION
The findings from this study are of high relevance for the further development of automatic linking programs that would allow the ICF-based classification of functioning properties within the International Classification of Diseases (ICD-11) for any health condition. Future research is needed to identify PROMs that are best suited to the automatic ICF translation process using machine learning algorithms.