Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Wu, Xuehong; Duan, Jiaqi; Pan, Yi; Li, Min

doi:10.26599/bdma.2022.9020021

Cited by 37 publications

(16 citation statements)

References 142 publications

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“…Using a knowledge graph for interconnecting data from biological data sources is not a novel idea. Knowledge graphs are the foundational structure for intelligent health care [ 18 ]. As mentioned in the related work section, many projects aim to transform a wide variety of biological data into knowledge graphs to capture the relations between objects in a convenient form and understand the deeper meaning of those relations.…”

Section: Resultsmentioning

confidence: 99%

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

Veljković

Orlov

Mitić

2023

IJMS

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Studying the association of gene function, diseases, and regulatory gene network reconstruction demands data compatibility. Data from different databases follow distinct schemas and are accessible in heterogenic ways. Although the experiments differ, data may still be related to the same biological entities. Some entities may not be strictly biological, such as geolocations of habitats or paper references, but they provide a broader context for other entities. The same entities from different datasets can share similar properties, which may or may not be found within other datasets. Joint, simultaneous data fetching from multiple data sources is complicated for the end-user or, in many cases, unsupported and inefficient due to differences in data structures and ways of accessing the data. We propose BioGraph—a new model that enables connecting and retrieving information from the linked biological data that originated from diverse datasets. We have tested the model on metadata collected from five diverse public datasets and successfully constructed a knowledge graph containing more than 17 million model objects, of which 2.5 million are individual biological entity objects. The model enables the selection of complex patterns and retrieval of matched results that can be discovered only by joining the data from multiple sources.

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Section: Resultsmentioning

confidence: 99%

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

Veljković

Orlov

Mitić

2023

IJMS

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“…Most of the prior work in building a medical KG has used scientific literature, such as PubMed and electronic medical records, and only specific types of entities, such as diseases, chemicals, and genes, have been considered [ 17 , 41 , 42 ]. Ernst et al [ 43 ] used patient-oriented online health portals to build a KG, indicating the importance of medical information spread across different sources.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study

Costello¹,

Kaur²,

Reformat³

et al. 2023

J Med Internet Res

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Background Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such group is individuals with neurodevelopmental disorders (NDDs) and their families. NDDs affect up to 18% of the population and have major social and economic impacts. The current limitations in communicating information for individuals with NDDs include the absence of shared terminology and the lack of efficient labeling processes for web resources. Because of these limitations, health professionals, support groups, and families are unable to share, combine, and access resources. Objective We aimed to develop a natural language–based pipeline to label resources by leveraging standard and free-text vocabularies obtained through text analysis, and then represent those resources as a weighted knowledge graph. Methods Using a combination of experts and service/organization databases, we created a data set of web resources for NDDs. Text from these websites was scraped and collected into a corpus of textual data on NDDs. This corpus was used to construct a knowledge graph suitable for use by both experts and nonexperts. Named entity recognition, topic modeling, document classification, and location detection were used to extract knowledge from the corpus. Results We developed a resource annotation pipeline using diverse natural language processing algorithms to annotate web resources and stored them in a structured knowledge graph. The graph contained 78,181 annotations obtained from the combination of standard terminologies and a free-text vocabulary obtained using topic modeling. An application of the constructed knowledge graph is a resource search interface using the ordered weighted averaging operator to rank resources based on a user query. Conclusions We developed an automated labeling pipeline for web resources on NDDs. This work showcases how artificial intelligence–based methods, such as natural language processing and knowledge graphs for information representation, can enhance knowledge extraction and mobilization, and could be used in other fields of medicine.

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“…With knowledge graphs, explanations are easy to extract [135], [136] because of the rich semantic relations inherent in the representation. This kind of explainaibility is especially popular in applications such as product recommender systems (e.g., [19], [137], [138]) drug recommendations [139], [140]) and disease diagnosis [141], [142]).…”

Section: Knowledge-informed Explainability Methodsmentioning

confidence: 99%

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

Mumuni

2022

Prog Artif Intell

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We review current and emerging knowledge-informed and brain-inspired cognitive systems for realizing adversarial defenses, eXplainable Artificial Intelligence (XAI), and zero-shot or few-shot learning. Data-driven deep learning models have achieved remarkable performance and demonstrated capabilities surpassing human experts in many applications. Yet, their inability to exploit domain knowledge leads to serious performance limitations in practical applications. In particular, deep learning systems are exposed to adversarial attacks, which can trick them into making glaringly incorrect decisions. Moreover, complex data-driven models typically lack interpretability or explainability, i.e., their decisions cannot be understood by human subjects. Furthermore, models are usually trained on standard datasets with a closed-world assumption. Hence, they struggle to generalize to unseen cases during inference in practical open-world environments, thus, raising the zero-or few-shot generalization problem. Although many conventional solutions exist, explicit domain knowledge, brain-inspired neural network and cognitive architectures offer powerful new dimensions towards alleviating these problems. Prior knowledge is represented in appropriate forms and incorporated in deep learning frameworks to improve performance. Brain-inspired cognition methods use computational models that mimic the human mind to enhance intelligent behavior in artificial agents and autonomous robots. Ultimately, these models achieve better explainability, higher adversarial robustness and data-efficient learning, and can, in turn, provide insights for cognitive science and neurosciencethat is, to deepen human understanding on how the brain works in general, and how it handles these problems.Index Terms-Domain knowledge, cognitive architecture, brain-inspired neural network, explainable AI, adversarial attack, zero-shot generalization. ✦ INTRODUCTION State of deep learningMachine learning is an artificial intelligence (AI) concept that enables computing systems to learn useful relationships from data and then use this information to identify learned patterns and make predictions on new inputs. Deep learning (DL) is a machine learning method that uses a multi-layered arrangement of computational units to learn relevant patterns from data. Many deep learning constructs have been proposed for various use-cases. Some of the most popular ones are deep convolutional neural networks (DCNN) [1], [2] recurrent neural networks (RNN) [3], generative adversarial networks (GANs) [4], deep reinforcement learning [5] and vision transformers [6], [7]). DCNNs are a class of deep neural networks (DNNs) specially designed to handle image data.Recently, deep learning methods have accomplished remarkable milestones in many critical applications. In medical domains, some state-of-the-art models (e.g., as reported

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Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Cited by 37 publications

References 142 publications

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

BioGraph: Data Model for Linking and Querying Diverse Biological Metadata

Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

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