Granviken Fredrik, Vasseljen Ottar, Bach Kerstin, Jaiswal Amar, Meisingset Ingebrigt
Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
Clinic of Rehabilitation, St Olavs Hospital, Trondheim, Norway.
JMIR Form Res. 2024 May 10;8:e44805. doi: 10.2196/44805.
Common interventions for musculoskeletal pain disorders either lack evidence to support their use or have small to modest or short-term effects. Given the heterogeneity of patients with musculoskeletal pain disorders, treatment guidelines and systematic reviews have limited transferability to clinical practice. A problem-solving method in artificial intelligence, case-based reasoning (CBR), where new problems are solved based on experiences from past similar problems, might offer guidance in such situations.
This study aims to use CBR to build a decision support system for patients with musculoskeletal pain disorders seeking physiotherapy care. This study describes the development of the CBR system SupportPrim PT and demonstrates its ability to identify similar patients.
Data from physiotherapy patients in primary care in Norway were collected to build a case base for SupportPrim PT. We used the local-global principle in CBR to identify similar patients. The global similarity measures are attributes used to identify similar patients and consisted of prognostic attributes. They were weighted in terms of prognostic importance and choice of treatment, where the weighting represents the relevance of the different attributes. For the local similarity measures, the degree of similarity within each attribute was based on minimal clinically important differences and expert knowledge. The SupportPrim PT'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 musculoskeletal pain. We also assessed the same in a more extensive case base.
The original case base contained 105 patients with musculoskeletal pain (mean age 46, SD 15 years; 77/105, 73.3% women). The SupportPrim PT 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 (95% CI 8.0-10.6) points on the ÖMSPQ and a mean absolute difference of 5.6 (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. Patients retrieved from a more extensive case base (N=486) had a higher mean similarity score and were slightly more similar to the query patients in ÖMSPQ and MSK-HQ compared with the original smaller case base.
This study describes the development of a CBR system, SupportPrim PT, for musculoskeletal pain in primary care. The SupportPrim PT identified similar patients according to an established screening tool and an outcome measure for patients with musculoskeletal pain.
肌肉骨骼疼痛疾病的常见干预措施要么缺乏证据支持其使用，要么效果较小、适中或只是短期有效。鉴于肌肉骨骼疼痛疾病患者的异质性，治疗指南和系统评价在临床实践中的可转移性有限。人工智能中的一种解决问题的方法，即基于案例的推理（CBR），根据过去类似问题的经验来解决新问题，可能在此类情况下提供指导。
本研究旨在使用基于案例的推理为寻求物理治疗护理的肌肉骨骼疼痛疾病患者建立一个决策支持系统。本研究描述了基于案例的推理系统SupportPrim PT的开发，并展示了其识别相似患者的能力。
收集挪威初级保健中物理治疗患者的数据，为SupportPrim PT建立案例库。我们在基于案例的推理中使用局部 - 全局原则来识别相似患者。全局相似性度量是用于识别相似患者的属性，由预后属性组成。它们根据预后重要性和治疗选择进行加权，其中权重代表不同属性的相关性。对于局部相似性度量，每个属性内的相似程度基于最小临床重要差异和专家知识。通过将案例库中所有患者的相似性得分与用于肌肉骨骼疼痛的既定筛查工具（Örebro肌肉骨骼疼痛筛查问卷简表[ÖMSPQ]）和结局指标（肌肉骨骼健康问卷[MSK - HQ]）的得分进行比较，评估SupportPrim PT识别相似患者的能力。我们还在一个更广泛的案例库中进行了同样的评估。
原始案例库包含105名肌肉骨骼疼痛患者（平均年龄46岁，标准差15岁；77/105，73.3%为女性）。SupportPrim PT由29个具有局部相似性的加权属性组成。在将案例库中所有患者的相似性得分一次一个地与ÖMSPQ和MSK - HQ进行比较时，最相似的患者与查询患者在ÖMSPQ上的平均绝对差异为9.3（95%CI 8.0 - 10.6）分，在MSK-HQ上的平均绝对差异为5.6（95%CI 4.6 - 6.6）分。对于ÖMSPQ和MSK - HQ，随着最相似患者排名的降低，绝对得分差异均增加。与原始较小的案例库相比，从更广泛的案例库（N = 486）中检索出的患者具有更高的平均相似性得分，并且在ÖMSPQ和MSK - HQ方面与查询患者的相似度略高。
本研究描述了用于初级保健中肌肉骨骼疼痛的基于案例的推理系统SupportPrim PT的开发。SupportPrim PT根据既定的筛查工具和肌肉骨骼疼痛患者的结局指标识别相似患者。
