From Symptoms to Diseases – Creating the Missing Link

Oberkampf, Heiner; Gojayev, Turan; Zillner, Sonja; Zühlke, Dietlind; Auer, Sören; Hammon, Matthias

doi:10.1007/978-3-319-18818-8_40

Cited by 9 publications

(2 citation statements)

References 16 publications

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“…This approach is gathering information based on the query and thus proposed ontology generated called HDDO is an upper-level ontology for personal health diagnosis, and it is used to identify possible diagnoses from the user queries and his personal data. In the work of [47], the disease symptoms relations were extracted by clustering algorithm based on structural disease symptoms relations' mentions from different medical ontologies. An automatic approach for constructing a knowledge base of symptoms in Chinese was discussed in [48] where a graph was built from Chinese data sources.…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…Despite these significant results, most of these KBs capture quite general medical knowledge while they contain few connections (relations) between their entities. For example, there is currently no comprehensive KB containing disease-symptom relations with only some preliminary and ad hoc manual efforts (Oberkampf et al 2015;Mhadhbi and Akaichi 2017), not to mention other types of relations like risk factors, disease interactions, and more. Consequently, besides their use as vocabularies for text annotation and/or standardisation purposes such sources have not so far formed the basis for supporting diagnostic tasks for which purposes they need to be extended or specialised (Rubin et al 2006).…”

Section: Introductionmentioning

confidence: 99%

A System for Medical Information Extraction and Verification from Unstructured Text

Jurić

Stoilos

Melo

et al. 2020

AAAI

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A wealth of medical knowledge has been encoded in terminologies like SNOMED CT, NCI, FMA, and more. However, these resources are usually lacking information like relations between diseases, symptoms, and risk factors preventing their use in diagnostic or other decision making applications. In this paper we present a pipeline for extracting such information from unstructured text and enriching medical knowledge bases. Our approach uses Semantic Role Labelling and is unsupervised. We show how we dealt with several deficiencies of SRL-based extraction, like copula verbs, relations expressed through nouns, and assigning scores to extracted triples. The system have so far extracted about 120K relations and in-house doctors verified about 5k relationships. We compared the output of the system with a manually constructed network of diseases, symptoms and risk factors build by doctors in the course of a year. Our results show that our pipeline extracts good quality and precise relations and speeds up the knowledge acquisition process considerably.

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