Accepted for/Published in: JMIR Formative Research
Date Submitted: Dec 4, 2022
Date Accepted: Mar 21, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Case-based reasoning: application of an Artificial Intelligence system in the management of common musculoskeletal pain complaints
ABSTRACT
Background:
Applying group-level evidence from guidelines to individual patients is challenging due to the great variation in symptoms in patients with musculoskeletal (MSK) pain complaints. A problem-solving method in artificial intelligence (AI), case-based reasoning (CBR), where new problems are solved based on experiences from past similar problems, might offer guidance in such situations.
Objective:
The objective of this study was to use CBR to build an AI system for decision support in musculoskeletal (MSK) pain patients seeking physiotherapy care. The paper describes the development of the CBR system and demonstrates the system’s ability to identify similar patients.
Methods:
Data from physiotherapy patients in primary care of Norway were collected to build a case base for the CBR system. We used the local-global principle in CBR to identify similar patients. The global similarity measures consisted of prognostic attributes, weighted in terms of prognostic importance and choice of treatment. For the local similarity measures, the degree of similarity within each attribute, was based on minimal clinically important difference and expert knowledge. The CBR system’s ability to identify similar patients was assessed by comparing the similarity scores of all patients in the case base with the scores on an established screening tool (The short form Örebro Musculoskeletal Pain Screening Questionnaire (ÖMSPQ)) and an outcome measure (The Musculoskeletal Health Questionnaire (MSK-HQ)) used in MSK pain.
Results:
The original case base contained 105 patients with MSK pain (mean age 46 years (SD 15); 73% women). The CBR system consisted of 29 weighted attributes with local similarities. When comparing the similarity scores for all patients in the case base, one at a time, with the ÖMSPQ and MSK-HQ, the most similar patients had a mean absolute difference from the query patient of 9.3 points (95% CI, 8.0-10.6 points) on the ÖMSPQ and a mean absolute difference of 5.6 points (95% CI, 4.6-6.6 points) on the MSK-HQ. For both ÖMSPQ and MSK-HQ, the absolute score difference increased as the rank of most similar patients decreased.
Conclusions:
This paper describes the development of a CBR system for MSK pain in primary care. The CBR system identified similar patients according to an established screening tool and an outcome measure for patients with MSK pain.
Citation
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