An exploration of knowledge-organizing technologies to advance transdisciplinary back pain research.
An exploration of knowledge-organizing technologies to advance transdisciplinary back pain research.
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DOI：
10.1002/jsp2.1300
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发表时间：
2023-12
期刊：
影响因子：
3.7
通讯作者：
Hunt, C. Anthony
中科院分区：
文献类型：
--
作者：
Lotz, Jeffrey C.;Ropella, Glen;Anderson, Paul;Yang, Qian;Hedderich, Michael A.;Bailey, Jeannie;Hunt, C. Anthony
慢性下腰痛(LBP)受到生物心理社会模型(BSM)领域中广泛的患者特有因素的影响。将BSM投入到研究和临床护理中是具有挑战性的，因为大多数研究人员都是在仅专注于一个或两个BSM领域的竖井中工作。此外，BSM研究的扩展和多学科性质造成了个人研究者如何将当前数据整合到他们产生影响的假设的过程中的实际限制。快速发展的人工智能(AI)领域正在为组织知识提供新的工具，但人工智能如何推进LBP研究和临床的实用方面正在开始探索。这项工作的目标是：(1)探索当前知识集成技术(大语言模型(LLM)、相似性图(SGS)和知识图(KGs))合成生物医学文献并描述BSM中反映的多模式关系的能力，以及(2)突出限制、实施细节和未来研究领域以提高性能。我们证明了初步的证据表明，LLMS，如GPT-3，可能有助于科学家分析和区分跨多个BSM结构域的cLBP出版物，并确定文献支持或反对新兴假说的程度。我们表明，SG表示和KG能够以新颖的方式探索LBP的文学，可能提供目前难以实现的跨学科视角或见解。SG方法是自动化的、简单的、执行成本低的，因此可能对超出专业领域的早期文学和叙事探索有用。同样，我们展示了KG可以使用自动化管道构建，查询以提供语义信息，并进行分析以探索跨域联系。给出的例子支持了LBP定制的人工智能协议组织知识并支持开发和提炼跨域假设的可行性。背痛的多学科性质使得将生物、心理和社会领域的信息整合到整体研究假说中变得困难。快速发展的人工智能(AI)领域正在为挖掘数据提供新的工具，但AI如何推进LBP研究和临床护理的实用方面尚未探索。我们证明了初步的证据表明，大型语言模型、相似性图和知识图可能有助于组织知识，并支持跨域假设的开发和完善。
Chronic low back pain (LBP) is influenced by a broad spectrum of patient‐specific factors as codified in domains of the biopsychosocial model (BSM). Operationalizing the BSM into research and clinical care is challenging because most investigators work in silos that concentrate on only one or two BSM domains. Furthermore, the expanding, multidisciplinary nature of BSM research creates practical limitations as to how individual investigators integrate current data into their processes of generating impactful hypotheses. The rapidly advancing field of artificial intelligence (AI) is providing new tools for organizing knowledge, but the practical aspects for how AI may advance LBP research and clinical are beginning to be explored. The goals of the work presented here are to: (1) explore the current capabilities of knowledge integration technologies (large language models (LLM), similarity graphs (SGs), and knowledge graphs (KGs)) to synthesize biomedical literature and depict multimodal relationships reflected in the BSM, and; (2) highlight limitations, implementation details, and future areas of research to improve performance. We demonstrate preliminary evidence that LLMs, like GPT‐3, may be useful in helping scientists analyze and distinguish cLBP publications across multiple BSM domains and determine the degree to which the literature supports or contradicts emergent hypotheses. We show that SG representations and KGs enable exploring LBP's literature in novel ways, possibly providing, trans‐disciplinary perspectives or insights that are currently difficult, if not infeasible to achieve. The SG approach is automated, simple, and inexpensive to execute, and thereby may be useful for early‐phase literature and narrative explorations beyond one's areas of expertise. Likewise, we show that KGs can be constructed using automated pipelines, queried to provide semantic information, and analyzed to explore trans‐domain linkages. The examples presented support the feasibility for LBP‐tailored AI protocols to organize knowledge and support developing and refining trans‐domain hypotheses. Multidisciplinary nature of back pain makes it difficult to integrate information across the biopsychosocial domains into holistic research hypotheses. The rapidly advancing field of artificial intelligence (AI) is providing new tools for mining data, but the practical aspects for how AI may advance LBP research and clinical care have yet to be explored. We demonstrate preliminary evidence that large language models, similarity graphs, and knowledge graphs may be useful to organize knowledge and support development and refinement of trans‐domain hypotheses.
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影响因子：
3.1
作者：
Chau, Anthony;Steib, Sharis;Whitaker, Evans;Kohns, David;Quinter, Alexander;Craig, Anita;Chiodo, Anthony;Chandran, SriKrishan;Laidlaw, Ann;Schott, Zachary;Farlow, Nathan;Yarjanian, John;Omwanghe, Ashley;Wasserman, Ronald;O'Neill, Conor;Clauw, Dan;Bowden, Anton;Marras, William;Carey, Tim;Mehling, Wolf;Hunt, C. Anthony;Lotz, Jeffrey
通讯作者：
Lotz, Jeffrey
影响因子：
3
作者：
Claeys, Kurt;Brumagne, Simon;Janssens, Lotte
通讯作者：
Janssens, Lotte
影响因子：
2.5
作者：
Flor, H;Braun, C;Birbaumer, N
通讯作者：
Birbaumer, N
影响因子：
3.7
作者：
Boucher JA;Abboud J;Nougarou F;Normand MC;Descarreaux M
通讯作者：
Descarreaux M
DOI：
10.1073/pnas.96.14.7705
发表时间：
1999-07-06
影响因子：
11.1
作者：
Bushnell, MC;Duncan, GH;Carrier, B
通讯作者：
Carrier, B