Knowledge Graphs and Explainable AI in Healthcare

Rajabi, Enayat; Kafaie, Somayeh

doi:10.3390/info13100459

Cited by 17 publications

(7 citation statements)

References 54 publications

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“…Neurosymbolic AI is a powerful interdisciplinary approach at the intersection of symbolic reasoning and neural network-based learning, aiming to harness the logical strengths and explicit knowledge representation of symbolic methods and the pattern recognition capabilities of neural networks (NNs). This hybrid model seamlessly integrates symbolic reasoning, often associated with rule-based systems, with subsymbolic learning, utilizing neural networks for effective pattern recognition [26]. One of its distinguishing features is the capability to create interpretable and explainable AI models by leveraging explicit rule-based representations from symbolic components.…”

Section: Neurosymbolic Ai and Gnnsmentioning

confidence: 99%

Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks

Zafeiropoulos,

Bitilis,

Tsekouras

et al. 2024

Information

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In the realm of Parkinson’s Disease (PD) research, the integration of wearable sensor data with personal health records (PHR) has emerged as a pivotal avenue for patient alerting and monitoring. This study delves into the complex domain of PD patient care, with a specific emphasis on harnessing the potential of wearable sensors to capture, represent and semantically analyze crucial movement data and knowledge. The primary objective is to enhance the assessment of PD patients by establishing a robust foundation for personalized health insights through the development of Personal Health Knowledge Graphs (PHKGs) and the employment of personal health Graph Neural Networks (PHGNNs) that utilize PHKGs. The objective is to formalize the representation of related integrated data, unified sensor and PHR data in higher levels of abstraction, i.e., in a PHKG, to facilitate interoperability and support rule-based high-level event recognition such as patient’s missing dose or falling. This paper, extending our previous related work, presents the Wear4PDmove ontology in detail and evaluates the ontology within the development of an experimental PHKG. Furthermore, this paper focuses on the integration and evaluation of PHKG within the implementation of a Graph Neural Network (GNN). This work emphasizes the importance of integrating PD-related data for monitoring and alerting patients with appropriate notifications. These notifications offer health experts precise and timely information for the continuous evaluation of personal health-related events, ultimately contributing to enhanced patient care and well-informed medical decision-making. Finally, the paper concludes by proposing a novel approach for integrating personal health KGs and GNNs for PD monitoring and alerting solutions.

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Section: Neurosymbolic Ai and Gnnsmentioning

confidence: 99%

Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks

Zafeiropoulos,

Bitilis,

Tsekouras

et al. 2024

Information

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“…By extracting features and relations, performing reasoning, and structuring information, knowledge graphs play a vital role in XAI for explainability. Rajabi et al [36]focused on the role of knowledge graphs in XAI models in healthcare. Based on their review, they asserted that knowledge graphs in XAI may be utilized for the detection of adverse drug reactions, drug-drug interactions, and healthcare misinformation and to mitigate the research gap between AI-based models and healthcare experts.…”

Section: Xai In Healthcare a Drug Discovery And Xaimentioning

confidence: 99%

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

Alizadehsani,

Oyelere,

Hussain

et al. 2024

IEEE Access

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The field of drug discovery has experienced a remarkable transformation with the advent of artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and ML models are becoming more complex, there is a growing need for transparency and interpretability of the models. Explainable Artificial Intelligence (XAI) is a novel approach that addresses this issue and provides a more interpretable understanding of the predictions made by machine learning models. In recent years, there has been an increasing interest in the application of XAI techniques to drug discovery. This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The article also covers the application of XAI in drug discovery, including target identification, compound design, and toxicity prediction. Furthermore, the article suggests potential future research directions for the application of XAI in drug discovery. This review article aims to provide a comprehensive understanding of the current state of XAI in drug discovery and its potential to transform the field.

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“…Knowledge graphs have proven to provide efficient and effective solutions to conceptualize a healthcare domain and thus be used for several healthcare systems [12]. Existing intelligent healthcare systems such as disease prediction and healthcare recommender systems lack a reliable knowledge base that has the power to represent heterogeneous data resources, having the ability to dynamically grow as more data is provided which could be visualized whenever needed [13][14][15][16][17][18]. The knowledge graph is an efficient representation of such a knowledge base.…”

Section: Knowledge Graph Constructionmentioning

confidence: 99%

An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

Maghawry

Ghoniemy

Shaaban

et al. 2023

BDCC

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Semantic data integration provides the ability to interrelate and analyze information from multiple heterogeneous resources. With the growing complexity of medical ontologies and the big data generated from different resources, there is a need for integrating medical ontologies and finding relationships between distinct concepts from different ontologies where these concepts have logical medical relationships. Standardized Medical Ontologies are explicit specifications of shared conceptualization, which provide predefined medical vocabulary that serves as a stable conceptual interface to medical data sources. Intelligent Healthcare systems such as disease prediction systems require a reliable knowledge base that is based on Standardized medical ontologies. Knowledge graphs have emerged as a powerful dynamic representation of a knowledge base. In this paper, a framework is proposed for automatic knowledge graph generation integrating two medical standardized ontologies- Human Disease Ontology (DO), and Symptom Ontology (SYMP) using a medical online website and encyclopedia. The framework and methodologies adopted for automatically generating this knowledge graph fully integrated the two standardized ontologies. The graph is dynamic, scalable, easily reproducible, reliable, and practically efficient. A subgraph for cancer terms is also extracted and studied for modeling and representing cancer diseases, their symptoms, prevention, and risk factors.

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Knowledge Graphs and Explainable AI in Healthcare

Cited by 17 publications

References 54 publications

Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks

Evaluating Ontology-Based PD Monitoring and Alerting in Personal Health Knowledge Graphs and Graph Neural Networks

Explainable Artificial Intelligence for Drug Discovery and Development: A Comprehensive Survey

An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

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