A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises

• URL: http://arxiv.org/abs/2306.04802v5
• Date: Sun, 02 Feb 2025 19:38:20 GMT
• Title: A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
• Authors: Hejie Cui, Jiaying Lu, Ran Xu, Shiyu Wang, Wenjing Ma, Yue Yu, Shaojun Yu, Xuan Kan, Chen Ling, Liang Zhao, Zhaohui S. Qin, Joyce C. Ho, Tianfan Fu, Jing Ma, Mengdi Huai, Fei Wang, Carl Yang,
• Abstract summary: This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs)<n>We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications.<n>The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice.
• Score: 59.4999994297993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science research, pharmaceutical research and development, clinical decision support, and public health. The review encompasses both model-free and model-based utilization approaches and the integration of HKGs with large language models (LLMs). We searched Google Scholar for relevant papers on HKGs and classified them into the following topics: HKG construction, HKG utilization, and their downstream applications in various domains. We also discussed their special challenges and the promise for future work. The review highlights the potential of HKGs to significantly impact biomedical research and clinical practice by integrating vast amounts of biomedical knowledge from multiple domains. The synergy between HKGs and LLMs offers promising opportunities for constructing more comprehensive knowledge graphs and improving the accuracy of healthcare applications. HKGs have emerged as a powerful tool for structuring medical knowledge, with broad applications across biomedical research, clinical decision-making, and public health. This survey serves as a roadmap for future research and development in the field of HKGs, highlighting the potential of combining knowledge graphs with advanced machine learning models for healthcare transformation.

Related papers

• Biomedical Foundation Model: A Survey [84.26268124754792]
  Foundation models are large-scale pre-trained models that learn from extensive unlabeled datasets. These models can be adapted to various applications such as question answering and visual understanding. This survey explores the potential of foundation models across diverse domains within biomedical fields.
  arXiv  Detail & Related papers  (2025-03-03T22:42:00Z)
• Merging Clinical Knowledge into Large Language Models for Medical Research and Applications: A Survey [9.359273536688066]
  Medical artificial intelligence (medical AI) aims to apply academic medical AI systems to real-world medical scenarios. This survey focuses on the building paradigms of medical AI systems including the use of clinical databases, datasets, training pipelines, integrating medical knowledge graphs, system applications, and evaluation systems.
  arXiv  Detail & Related papers  (2025-02-28T12:00:51Z)
• Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications [1.3749490831384268]
  Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods, overlooking the broader landscape and the rapid technological progress reshaping it. This survey offers a systematic review of BKGs from three core perspectives: domains, tasks, and applications.
  arXiv  Detail & Related papers  (2025-01-20T18:02:03Z)
• Distill-SynthKG: Distilling Knowledge Graph Synthesis Workflow for Improved Coverage and Efficiency [59.6772484292295]
  Knowledge graphs (KGs) generated by large language models (LLMs) are increasingly valuable for Retrieval-Augmented Generation (RAG) applications. Existing KG extraction methods rely on prompt-based approaches, which are inefficient for processing large-scale corpora. We propose SynthKG, a multi-step, document-level synthesis KG workflow based on LLMs. We also design a novel graph-based retrieval framework for RAG.
  arXiv  Detail & Related papers  (2024-10-22T00:47:54Z)
• LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic Pathologies [3.2221734920470797]
  We propose a Vision-Language framework augmented with a Knowledge Graph (KG)-based datastore to generate Natural Language Explanations (NLEs) for medical images. Our framework employs a KG-based retrieval mechanism that not only improves the precision of the generated explanations but also preserves data privacy by avoiding direct data retrieval. These frameworks are validated on the MIMIC-NLE dataset, where they achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-07T04:59:08Z)
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval [61.70489848327436]
  KARE is a novel framework that integrates knowledge graph (KG) community-level retrieval with large language models (LLMs) reasoning. Extensive experiments demonstrate that KARE outperforms leading models by up to 10.8-15.0% on MIMIC-III and 12.6-12.7% on MIMIC-IV for mortality and readmission predictions.
  arXiv  Detail & Related papers  (2024-10-06T18:46:28Z)
• Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation [9.286509119104563]
  We introduce a novel graph-based Retrieval-Augmented Generation framework specifically designed for the medical domain, called MedGraphRAG. Our approach is validated on 9 medical Q&A benchmarks, 2 health fact-checking benchmarks, and one collected dataset testing long-form generation.
  arXiv  Detail & Related papers  (2024-08-08T03:11:12Z)
• Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering [87.67177556994525]
  We propose a training-free method called Generate-on-Graph (GoG) to generate new factual triples while exploring Knowledge Graphs (KGs) GoG performs reasoning through a Thinking-Searching-Generating framework, which treats LLM as both Agent and KG in IKGQA.
  arXiv  Detail & Related papers  (2024-04-23T04:47:22Z)
• Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications [2.913761513290171]
  Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs.
  arXiv  Detail & Related papers  (2024-02-20T00:07:55Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer [0.9437165725355702]
  A challenging scenarios for artificial intelligence (AI) is using biomedical data to provide diagnosis and treatment recommendations for cancerous conditions. A large-scale knowledge graph (KG) can be constructed by integrating and extracting facts about semantically interrelated entities and relations. In this paper, we develop a domain KG to leverage cancer-specific biomarker discovery and interactive QA.
  arXiv  Detail & Related papers  (2023-10-12T14:36:13Z)
• Artificial General Intelligence for Medical Imaging Analysis [92.3940918983821]
  Large-scale Artificial General Intelligence (AGI) models have achieved unprecedented success in a variety of general domain tasks. These models face notable challenges arising from the medical field's inherent complexities and unique characteristics. This review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.
  arXiv  Detail & Related papers  (2023-06-08T18:04:13Z)
• HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level [7.96433065992062]
  Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. We propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention. Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets.
  arXiv  Detail & Related papers  (2023-05-11T05:59:31Z)
• Healthcare Knowledge Graph Construction: State-of-the-art, open issues, and opportunities [5.652978777706895]
  This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. A thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. Several research findings and existing issues in the literature are reported and discussed.
  arXiv  Detail & Related papers  (2022-07-08T09:19:01Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.