Structural technology research on symptom data of Chinese medicine

Aziguli,; Zhang, YuanYu; Xie, Ying; Xu, Yang; Chen, YuJia

doi:10.1109/healthcom.2017.8210797

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…A recent survey on knowledge-based biomedical data science 23 highlights the application of KGs in the biomedical and clinical domain in improving the retrieval of information from large sources of clinical data or literature [24][25][26] , providing evidence to support phenomena observed in data 27,28 , using link prediction to complete missing information and hypothesize previously unknown relationships 29 , and improving patient data representation [30][31][32] . In the biomedical domain, IE pipelines have been developed for the extraction of drug-disease relations 33,34 and disease-symptom relations 35 from biomedical literature.…”

Section: Related Workmentioning

confidence: 99%

From language models to large-scale food and biomedical knowledge graphs

Cenikj

Strojnik

Angelski³

et al. 2023

Sci Rep

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Knowledge about the interactions between dietary and biomedical factors is scattered throughout uncountable research articles in an unstructured form (e.g., text, images, etc.) and requires automatic structuring so that it can be provided to medical professionals in a suitable format. Various biomedical knowledge graphs exist, however, they require further extension with relations between food and biomedical entities. In this study, we evaluate the performance of three state-of-the-art relation-mining pipelines (FooDis, FoodChem and ChemDis) which extract relations between food, chemical and disease entities from textual data. We perform two case studies, where relations were automatically extracted by the pipelines and validated by domain experts. The results show that the pipelines can extract relations with an average precision around 70%, making new discoveries available to domain experts with reduced human effort, since the domain experts should only evaluate the results, instead of finding, and reading all new scientific papers.

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Section: Related Workmentioning

confidence: 99%

From language models to large-scale food and biomedical knowledge graphs

Cenikj

Strojnik

Angelski³

et al. 2023

Sci Rep

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“…KGs are well suited for transdisciplinary LBP research as KG‐based systems make it possible to find facts or evidence across multiple domains. 31 For example, Xie and colleagues integrated patient‐specific Chinese medicine through KGs 32 while Shang et al explored personalized medication recommendations by creating visit‐level representations of patients from Electronic Health Record data. 33 KGs also assist in hypothesis formation and, importantly, can do so across disciplinary boundaries.…”

Section: The Potential Of Knowledge Organizing Technologies For Trans...mentioning

confidence: 99%

An exploration of knowledge‐organizing technologies to advance transdisciplinary back pain research

Lotz,

Ropella,

Anderson

et al. 2023

JOR Spine

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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.

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Structural technology research on symptom data of Chinese medicine

Cited by 2 publications

References 1 publication

From language models to large-scale food and biomedical knowledge graphs

From language models to large-scale food and biomedical knowledge graphs

An exploration of knowledge‐organizing technologies to advance transdisciplinary back pain research

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