Semantic patient information aggregation and medicinal decision support

Potter, Pieterjan De; Cools, H; Depraetere, Kristof; Mels, Giovanni; Debevere, Pedro; Roo, Jos De; Huszka, Csaba; Colaert, Dirk; Mannens, Erik; Walle, Rik Van de

doi:10.1016/j.cmpb.2012.04.002

Cited by 27 publications

(14 citation statements)

References 19 publications

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“…drug-target relations (Sun, 2015), drug-disease relations (Sun, 2015), disease-gene relations (Sun, 2015), and disease-disease risk relations (Xu, Li, & Wang, 2014). (Sun, 2015;Xu, Li, & Wang, 2014) have been developed for a variety of biomedical applications like biomedical question answering (Athenikos & Han, 2010), clinical decision support (De Potter, et al, 2012) and the automation of computed tomography procedures (De Silva, MacDonald, Paterson, Sikdar, & Cochrane, 2011). Some of these resources such as BabelMeSH and SNOMED-CT are currently available in multiple languages and can be used to process biomedical data provided in languages other than English (Liu, Fontelo, & Ackerman, 2006;Henriksson, Skeppstedt, Kvist, Duneld, & Conway, 2013).…”

Section: Introductionmentioning

confidence: 99%

Wikidata: A large-scale collaborative ontological medical database

Turki

Shafee

Taieb

et al. 2019

Journal of Biomedical Informatics

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is a flexible biomedical semantic knowledge-base  It has the potential to perform a much better role as a large-scale biomedical semantic resource  Tools are available to query, enrich, verify and process the biomedical knowledge Abstract: Created in October 2012, Wikidata is a large-scale, human-readable, machine-readable, multilingual, multidisciplinary, centralized, editable, structured, and linked knowledge-base with an increasing diversity of use cases. Here, we raise awareness of the potential use of Wikidata as a useful resource for biomedical data integration and semantic interoperability between biomedical computer systems. We show the data model and characteristics of Wikidata and explain how this database can be automatically processed by users as well as by computer methods and programs. Then, we give an overview of the medical entities and relations provided by the database and how they can be useful for various medical purposes such as clinical decision support

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Section: Introductionmentioning

confidence: 99%

Wikidata: A large-scale collaborative ontological medical database

Turki

Shafee

Taieb

et al. 2019

Journal of Biomedical Informatics

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“…Over the recent years, ontologies have been extensively used in biomedical domains [1] such as annotation of medical records [2], medical knowledge representation and sharing [3], clinical data integration and medical decision making [4]. The vast usage of ontologies in biomedical domain has compelled researchers to develop more biomedical ontologies such as gene ontology (GO) [5], National Cancer Institute (NCI) thesaurus [6], Foundation Model of Anatomy (FMA) [7] and the Systemised Nomenclature of Medicine (SNOMED‐CT) [8].…”

Section: Introductionmentioning

confidence: 99%

Using NSGA‐III for optimising biomedical ontology alignment

Xue

Chen

2019

CAAI Transactions on Intelligence Technology

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“…Ontologies have gained much importance in the past two decades, especially in the biomedical domain. Various biomedical ontologies such as Gene Ontology (GO) [1], National Cancer Institute (NCI) Thesaurus [2], Foundation Model of Anatomy (FMA) [3], and Systemized Nomenclature of Medicine (SNOMED-CT) [4] have emerged and been maintained, which have been widely used in the medical records annotation [5], medical data formats standardization [6], medical or clinical knowledge representation and integration [7], and medical decision making [8]. Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information.…”

Section: Introductionmentioning

confidence: 99%

Interactive biomedical ontology matching

2019

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Due to continuous evolution of biomedical data, biomedical ontologies are becoming larger and more complex, which leads to the existence of many overlapping information. To support semantic inter-operability between ontology-based biomedical systems, it is necessary to identify the correspondences between these information, which is commonly known as biomedical ontology matching. However, it is a challenge to match biomedical ontologies, which dues to: (1) biomedical ontologies often possess tens of thousands of entities, (2) biomedical terminologies are complex and ambiguous. To efficiently match biomedical ontologies, in this paper, an interactive biomedical ontology matching approach is proposed, which utilizes the Evolutionary Algorithm (EA) to implement the automatic matching process, and gets a user involved in the evolving process to improve the matching efficiency. In particular, we propose an Evolutionary Tabu Search (ETS) algorithm, which can improve EA’s performance by introducing the tabu search algorithm as a local search strategy into the evolving process. On this basis, we further make the ETS-based ontology matching technique cooperate with the user in a reasonable amount of time to efficiently create high quality alignments, and make use of EA’s survival of the fittest to eliminate the wrong correspondences brought by erroneous user validations. The experiment is conducted on the Anatomy track and Large Biomedic track that are provided by the Ontology Alignment Evaluation Initiative (OAEI), and the experimental results show that our approach is able to efficiently exploit the user intervention to improve its non-interactive version, and the performance of our approach outperforms the state-of-the-art semi-automatic ontology matching systems.

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Semantic patient information aggregation and medicinal decision support

Cited by 27 publications

References 19 publications

Wikidata: A large-scale collaborative ontological medical database

Wikidata: A large-scale collaborative ontological medical database

Using NSGA‐III for optimising biomedical ontology alignment

Interactive biomedical ontology matching

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