Workshop (Knowledge Generation) ID 2001657

IF 2.4 Q1 REHABILITATION Topics in Spinal Cord Injury Rehabilitation Pub Date : 2023-09-01 DOI:10.46292/sci23-2001657s
Nader Fallah, H. Hong, S. Humphreys, Jessica Parsons, Kristen Walden, Vanessa K. Noonan
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Abstract

Multi-morbidity is common in persons with spinal cord injury (SCI). Network Analysis is a tool used to visualize and estimate complex relationships among variables. Three network models: Gaussian Graphical Model, Ising model, and Mixed Graphical Model were applied to the 2011-2012 Canadian SCI Community Survey dataset, which included individuals with traumatic and non-traumatic SCI. Data utilized included demographic and injury data as we well as 30 secondary health conditions (comorbidities and secondary complication) that are included in the Multi-Morbidity Index (MMI-30). Five health outcomes were included: healthcare utilization (HCU), health status (i.e. Short Form-12 physical and mental component summary (SF-12 PCS & MCS) score), life satisfaction, and quality of life. Using Network Analysis, we reduced the number of items in the Multi-Morbidity Index (MMI-30) by 5 items (MMI-25) and the psychometric properties were comparable. This interactive workshop will include presentations from a clinician, researcher and person with lived experience (PLEX). The goals of this workshop are to: This workshop will demonstrate the benefit of using Network Analysis, a type of Machine Learning, in SCI research. Specifically, the example of how Network Analysis identified key associations among 30 secondary health conditions and five health outcomes which resulted in the MMI-25 will be discussed as well as future opportunities for using Network Analysis and other Machine Learning methodologies in SCI research.
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讲习班(知识生成) ID 2001657
脊髓损伤(SCI)患者普遍患有多种疾病。网络分析是一种用于直观显示和估计变量间复杂关系的工具。有三种网络模型:高斯图形模型、伊辛模型和混合图形模型被应用于 2011-2012 年加拿大 SCI 社区调查数据集,其中包括创伤性和非创伤性 SCI 患者。所使用的数据包括人口统计学和损伤数据,以及多病指数(MMI-30)中包含的 30 种次要健康状况(合并症和次要并发症)。其中包括五项健康结果:医疗保健利用率(HCU)、健康状况(即简表-12 身体和精神部分摘要(SF-12 PCS 和 MCS)得分)、生活满意度和生活质量。利用网络分析法,我们将多病指数(MMI-30)中的项目数量减少了 5 个(MMI-25),其心理测量特性具有可比性。 本次互动研讨会将包括临床医生、研究人员和有生活经验者 (PLEX) 的演讲。 本次研讨会的目标是 本次研讨会将展示在 SCI 研究中使用网络分析(一种机器学习)的益处。具体来说,将讨论网络分析如何确定 30 种次要健康状况和五种健康结果之间的关键关联,从而形成 MMI-25 的例子,以及在 SCI 研究中使用网络分析和其他机器学习方法的未来机会。
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来源期刊
CiteScore
3.20
自引率
3.40%
发文量
33
期刊介绍: Now in our 22nd year as the leading interdisciplinary journal of SCI rehabilitation techniques and care. TSCIR is peer-reviewed, practical, and features one key topic per issue. Published topics include: mobility, sexuality, genitourinary, functional assessment, skin care, psychosocial, high tetraplegia, physical activity, pediatric, FES, sci/tbi, electronic medicine, orthotics, secondary conditions, research, aging, legal issues, women & sci, pain, environmental effects, life care planning
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