2,855 research outputs found
Knowledge graphs for covid-19: An exploratory review of the current landscape
Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research
Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining
Biomedical knowledge is growing in an astounding pace with a majority of this
knowledge is represented as scientific publications. Text mining tools and
methods represents automatic approaches for extracting hidden patterns and
trends from this semi structured and unstructured data. In Biomedical Text
mining, Literature Based Discovery (LBD) is the process of automatically
discovering novel associations between medical terms otherwise mentioned in
disjoint literature sets. LBD approaches proven to be successfully reducing the
discovery time of potential associations that are hidden in the vast amount of
scientific literature. The process focuses on creating concept profiles for
medical terms such as a disease or symptom and connecting it with a drug and
treatment based on the statistical significance of the shared profiles. This
knowledge discovery approach introduced in 1989 still remains as a core task in
text mining. Currently the ABC principle based two approaches namely open
discovery and closed discovery are mostly explored in LBD process. This review
starts with general introduction about text mining followed by biomedical text
mining and introduces various literature resources such as MEDLINE, UMLS, MESH,
and SemMedDB. This is followed by brief introduction of the core ABC principle
and its associated two approaches open discovery and closed discovery in LBD
process. This review also discusses the deep learning applications in LBD by
reviewing the role of transformer models and neural networks based LBD models
and its future aspects. Finally, reviews the key biomedical discoveries
generated through LBD approaches in biomedicine and conclude with the current
limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table
PubMed and Beyond: Recent Advances and Best Practices in Biomedical Literature Search
Biomedical research yields a wealth of information, much of which is only
accessible through the literature. Consequently, literature search is an
essential tool for building on prior knowledge in clinical and biomedical
research. Although recent improvements in artificial intelligence have expanded
functionality beyond keyword-based search, these advances may be unfamiliar to
clinicians and researchers. In response, we present a survey of literature
search tools tailored to both general and specific information needs in
biomedicine, with the objective of helping readers efficiently fulfill their
information needs. We first examine the widely used PubMed search engine,
discussing recent improvements and continued challenges. We then describe
literature search tools catering to five specific information needs: 1.
Identifying high-quality clinical research for evidence-based medicine. 2.
Retrieving gene-related information for precision medicine and genomics. 3.
Searching by meaning, including natural language questions. 4. Locating related
articles with literature recommendation. 5. Mining literature to discover
associations between concepts such as diseases and genetic variants.
Additionally, we cover practical considerations and best practices for choosing
and using these tools. Finally, we provide a perspective on the future of
literature search engines, considering recent breakthroughs in large language
models such as ChatGPT. In summary, our survey provides a comprehensive view of
biomedical literature search functionalities with 36 publicly available tools.Comment: 27 pages, 6 figures, 36 tool
Narrative visualization with augmented reality
The following study addresses, from a design perspective, narrative visualization using augmented reality (AR) in real physical spaces, and specifically in spaces with no semantic relation with the represented data. We intend to identify the aspects augmented reality adds, as narrative possibilities, to data visualization. Particularly, we seek to identify the aspects augmented reality introduces regarding the three dimensions of narrative visualization—view, focus and sequence. For this purpose, we adopted a comparative analysis of a set of fifty case studies, specifically, narrative visualizations using augmented reality from a journalistic scope, where narrative is a key feature. Despite the strong explanatory character that characterizes the set of analyzed cases, which sometimes limits the user’s agency, there is a strong interactive factor. It was found that augmented reality can expand the narrative possibilities in the three dimensions mentioned—view, focus and sequence—but especially regarding visual strategies where simulation plays an essential role. As a visual strategy, simulation can provide the context for communication or be the object of communication itself, as a replica.publishe
Navigating Healthcare Insights: A Birds Eye View of Explainability with Knowledge Graphs
Knowledge graphs (KGs) are gaining prominence in Healthcare AI, especially in
drug discovery and pharmaceutical research as they provide a structured way to
integrate diverse information sources, enhancing AI system interpretability.
This interpretability is crucial in healthcare, where trust and transparency
matter, and eXplainable AI (XAI) supports decision making for healthcare
professionals. This overview summarizes recent literature on the impact of KGs
in healthcare and their role in developing explainable AI models. We cover KG
workflow, including construction, relationship extraction, reasoning, and their
applications in areas like Drug-Drug Interactions (DDI), Drug Target
Interactions (DTI), Drug Development (DD), Adverse Drug Reactions (ADR), and
bioinformatics. We emphasize the importance of making KGs more interpretable
through knowledge-infused learning in healthcare. Finally, we highlight
research challenges and provide insights for future directions.Comment: IEEE AIKE 2023, 8 Page
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