AI-Powered Knowledge Graphs for Efficient Medical Information Retrieval and Decision Support Article Swipe
Santanu Kumar Sahoo
,
M. V. Sruthi
,
Varun Ojha
,
Vaibhav Kaushik
,
Manti Debnath
,
S RenukaJyothi
,
Naresh Kaushik
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.56294/mw2024517
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.56294/mw2024517
The enormous volume of medical data has resulted in the development of sophisticated systems that facilitate information search and enable clinicians in decision-making process. Driven by artificial intelligence, knowledge graphs (KGs) provide a solid structure for organising and evaluating vast volumes of diverse medical data, therefore enabling wiser question development and improved decision-making. This article presents a whole strategy for integrating knowledge graphs with artificial intelligence-based approaches to improve medical information search and decision support systems performance. Graph-based reasoning, natural language processing (NLP), and machine learning all help the proposed approach to enhance semantic comprehension. It achieves this by tying together unorganised and organised medical data sources to provide pertinent analysis. Using predictive analytics, personalised healthcare recommendations, and real-time clinical decision support, the AI-powered knowledge graph architecture helps you It achieves this by continuously shifting the relationships among illnesses, symptoms, therapies, pasts of patients. This approach also ensures that many healthcare systems may cooperate better, which facilitates information search and reduces the diagnostic error count. Including reinforcement learning techniques enhances question results depending on user interaction, therefore enhancing the search process. The results of experiments show that KGs with AI work better than traditional database-driven methods when it comes to getting medical information quickly, correctly, and usefully. The suggested method helps healthcare workers a lot by making it easier for them to get accurate, evidence-based information more quickly. This will eventually lead to better patient results. This study shows that knowledge graphs driven by AI have the ability to completely change how medical information is managed and how decisions are made. This could lead to smarter and more flexible healthcare systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.56294/mw2024517
- OA Status
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- OpenAlex ID
- https://openalex.org/W4408317501
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https://openalex.org/W4408317501Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.56294/mw2024517Digital Object Identifier
- Title
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AI-Powered Knowledge Graphs for Efficient Medical Information Retrieval and Decision SupportWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2024Year of publication
- Publication date
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2024-12-31Full publication date if available
- Authors
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Santanu Kumar Sahoo, M. V. Sruthi, Varun Ojha, Vaibhav Kaushik, Manti Debnath, S RenukaJyothi, Naresh KaushikList of authors in order
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https://doi.org/10.56294/mw2024517Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.56294/mw2024517Direct OA link when available
- Concepts
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Medical information, Computer science, Decision support system, Information retrieval, Knowledge management, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.31177845 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |