This paper presents a method to discover initial global similarity weights while developing a case-based reasoning (CBR) system. The approach is based on multiple feature relevance scoring methods and the relevance of features within each scoring method. The objective of this work is to utilize the characteristics of a dataset when creating similarity measures. The primary advantage of this method lies in its data-driven approach in the absence of domain knowledge in the early phase of a CBR system development. The results obtained based on the experiments on multiple public datasets show that the method improves the performance of similarity measures for a CBR system in discriminating relevant similar cases. Evaluation of the results is based on the method suitable for unbalanced datasets.

Case-based reasoning (CBR) is a problem-solving methodology in artificial intelligence that attempts to solve new problems using past experiences known as cases. Experiences collected in a single case base from an institution or geographical region are seldom sufficient to solve diverse problems, especially in rare situations. Additionally, many institutions do not promote peer-to-peer (p2p) communication or encourage data sharing through such networks to retain autonomy. The paper proposes a federated CBR (F-CBR) architecture to address these challenges. F-CBR enables solving new problems based on similar cases from multiple autonomous CBR systems without p2p communication. We also designed an algorithm to minimize (irrelevant or unsolicited) data sharing in an F-CBR system. We extend the F-CBR design to support institutions with organizational or geographical hierarchies. The F-CBR architecture was implemented and evaluated on two public datasets and a private real-world (non-specific musculoskeletal disorder patient) dataset. The findings demonstrate that the retrieval quality of F-CBR systems is comparable to or better than a single CBR system that persists all the cases on a centralized case base. F-CBR systems address data privacy by incorporating the data minimization principle. We foresee F-CBR as a viable real-world design that can aid in federating legacy CBR systems with minimal or no changes. The CBR systems used in this study are shared on GitHub to support reproducibility. INDEX TERMSCase-based reasoning, data minimization, data privacy, data silos, decision support systems, federated architecture, federated case-based reasoning. TABLE 1. Literature comparison table.

No 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.