Spark-MCA: Large-scale, Exhaustive Formal Concept Analysis for Evaluating the Semantic Completeness of SNOMED CT.

AMIA Annu Symp Proc. 2017;2017:1931-1940

Authors: Wei Z, Licong C, Guo-Qiang Z

Abstract
The completeness of a medical terminology system consists of two parts: complete content coverage and complete semantics. In this paper, we focus on semantic completeness and present a scalable approach, called Spark-MCA, for evaluating the semantic completeness of SNOMED CT. We formulate the SNOMED CT contents into an FCA-based formal context, in which SNOMED CT concepts are used for extents, while their attributes are used as intents. We applied Spark-MCA to the 201403 US edition of SNOMED CT to exhaustively compute all the formal concepts and sub concept relationships in about 2 hours with 96 processors using an Amazon Web Service cluster. We found a total of 799,868 formal concepts, within which 500,583 are not contained in the 201403 release. We compared these concepts with the cumulative addition of 22,687 concepts from the 5 "delta" files from the 201403 release to the 201609 release. 3,231 matches were found between those suggested by FCA and those from cumulative concept addition by the SNOMED CT Editorial Panel. This result provides encouraging evidence that our approach could be useful for enhancing the semantic completeness of SNOMED CT.

PMID: 29854265 [PubMed - in process]

Modeling Contextual Knowledge for Clinical Decision Support.

AMIA Annu Symp Proc. 2017;2017:1617-1624

Authors: Sordo M, Tokachichu P, Vitale CJ, Maviglia SM, Rocha RA

Abstract
In theory, the logic of decision rules should be atomic. In practice, this is not always possible; initially simple logic statements tend to be overloaded with additional conditions restricting the scope of such rules. By doing so, the original logic soon becomes encumbered with contextual knowledge. Contextual knowledge is re-usable on its own and could be modeled separately from the logic of a rule without losing the intended functionality. We model constraints to explicitly define the context where knowledge of decision rules is actionable. We borrowed concepts from Semantic Web, Complex Adaptive Systems, and Contextual Reasoning. The proposed approach provides the means for identifying and modeling contextual knowledge in a simple, sound manner. The methodology presented herein facilitates rule authoring, fosters consistency in rules implementation and maintenance; facilitates developing authoritative knowledge repositories to promote quality, safety and efficacy of healthcare; and paves the road for future work in knowledge discovery.

PMID: 29854232 [PubMed - in process]

Reconciliation of multiple guidelines for decision support: a case study on the multidisciplinary management of breast cancer within the DESIREE project.

AMIA Annu Symp Proc. 2017;2017:1527-1536

Authors: Séroussi B, Guézennec G, Lamy JB, Muro N, Larburu N, Sekar BD, Prebet C, Bouaud J

Abstract
Breast cancer is the most common cancer among women. DESIREE is a European project which aims at developing web-based services for the management of primary breast cancer by multidisciplinary breast units (BUs). We describe the guideline-based decision support system (GL-DSS) of the project. Various breast cancer clinical practice guidelines (CPGs) have been selected to be concurrently applied to provide state-of-the-art patient-specific recommendations. The aim is to reconcile CPG recommendations with the objective of complementarity to enlarge the number of clinical situations covered by the GL-DSS. Input and output data exchange with the GL-DSS is performed using FHIR. We used a knowledge model of the domain as an ontology on which relies the reasoning process performed by rules that encode the selected CPGs. Semantic web tools were used, notably the Euler/EYE inference engine, to implement the GL-DSS. "Rainbow boxes" are a synthetic tabular display used to visualize the inferred recommendations.

PMID: 29854222 [PubMed - in process]

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms.

AMIA Annu Symp Proc. 2017;2017:1352-1361

Authors: Papež V, Denaxas S, Hemingway H

Abstract
Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.

PMID: 29854204 [PubMed - in process]

Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud.

AMIA Annu Symp Proc. 2017;2017:1014-1023

Authors: Kamdar MR, Musen MA

Abstract
Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug-drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug-ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7-0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.

PMID: 29854169 [PubMed - in process]

Enhanced functionalities for annotating and indexing clinical text with the NCBO Annotator.

Bioinformatics. 2018 Jun 01;34(11):1962-1965

Authors: Tchechmedjiev A, Abdaoui A, Emonet V, Melzi S, Jonnagaddala J, Jonquet C

Abstract
Summary: Second use of clinical data commonly involves annotating biomedical text with terminologies and ontologies. The National Center for Biomedical Ontology Annotator is a frequently used annotation service, originally designed for biomedical data, but not very suitable for clinical text annotation. In order to add new functionalities to the NCBO Annotator without hosting or modifying the original Web service, we have designed a proxy architecture that enables seamless extensions by pre-processing of the input text and parameters, and post processing of the annotations. We have then implemented enhanced functionalities for annotating and indexing free text such as: scoring, detection of context (negation, experiencer, temporality), new output formats and coarse-grained concept recognition (with UMLS Semantic Groups). In this paper, we present the NCBO Annotator+, a Web service which incorporates these new functionalities as well as a small set of evaluation results for concept recognition and clinical context detection on two standard evaluation tasks (Clef eHealth 2017, SemEval 2014).
Availability and implementation: The Annotator+ has been successfully integrated into the SIFR BioPortal platform-an implementation of NCBO BioPortal for French biomedical terminologies and ontologies-to annotate English text. A Web user interface is available for testing and ontology selection (http://bioportal.lirmm.fr/ncbo_annotatorplus); however the Annotator+ is meant to be used through the Web service application programming interface (http://services.bioportal.lirmm.fr/ncbo_annotatorplus). The code is openly available, and we also provide a Docker packaging to enable easy local deployment to process sensitive (e.g. clinical) data in-house (https://github.com/sifrproject).
Contact: andon.tchechmedjiev@lirmm.fr.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 29846492 [PubMed - in process]

Web pages: What can you see in a single fixation?

Cogn Res Princ Implic. 2018;3(1):14

Authors: Jahanian A, Keshvari S, Rosenholtz R

Abstract
Research in human vision suggests that in a single fixation, humans can extract a significant amount of information from a natural scene, e.g. the semantic category, spatial layout, and object identities. This ability is useful, for example, for quickly determining location, navigating around obstacles, detecting threats, and guiding eye movements to gather more information. In this paper, we ask a new question: What can we see at a glance at a web page - an artificial yet complex "real world" stimulus? Is it possible to notice the type of website, or where the relevant elements are, with only a glimpse? We find that observers, fixating at the center of a web page shown for only 120 milliseconds, are well above chance at classifying the page into one of ten categories. Furthermore, this ability is supported in part by text that they can read at a glance. Users can also understand the spatial layout well enough to reliably localize the menu bar and to detect ads, even though the latter are often camouflaged among other graphical elements. We discuss the parallels between web page gist and scene gist, and the implications of our findings for both vision science and human-computer interaction.

PMID: 29774229 [PubMed]

Standard Lexicons, Coding Systems and Ontologies for Interoperability and Semantic Computation in Imaging.

J Digit Imaging. 2018 May 03;:

Authors: Wang KC

Abstract
Standard clinical terms, codes, and ontologies promote clarity and interoperability. Within radiology, there is a variety of relevant content resources, tools and technologies. These provide the basis for fundamental imaging workflows such as reporting and billing, and also facilitate a range of applications in quality improvement and research. This article reviews the key characteristics of lexicons, coding systems, and ontologies. A number of standards are described, including International Classification of Diseases-10-Clinical Modification (ICD-10-CM), Current Procedural Terminology (CPT), Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT), Logical Observation Identifiers Names and Codes (LOINC), and RadLex. Tools for accessing this material are reviewed, such as the National Center for Biomedical Ontology BioPortal system. Web services are discussed as a mechanism for semantic application development. Several example systems, workflows, and research applications using semantic technology are also surveyed.

PMID: 29725962 [PubMed - as supplied by publisher]

The semantic distance task: Quantifying semantic distance with semantic network path length.

J Exp Psychol Learn Mem Cogn. 2017 Sep;43(9):1470-1489

Authors: Kenett YN, Levi E, Anaki D, Faust M

Abstract
Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We propose a novel approach to computing semantic distance, based on network science methodology. Path length in a semantic network represents the amount of steps needed to traverse from 1 word in the network to the other. We examine whether path length can be used as a measure of semantic distance, by investigating how path length affect performance in a semantic relatedness judgment task and recall from memory. Our results show a differential effect on performance: Up to 4 steps separating between word-pairs, participants exhibit an increase in reaction time (RT) and decrease in the percentage of word-pairs judged as related. From 4 steps onward, participants exhibit a significant decrease in RT and the word-pairs are dominantly judged as unrelated. Furthermore, we show that as path length between word-pairs increases, success in free- and cued-recall decreases. Finally, we demonstrate how our measure outperforms computational methods measuring semantic distance (LSA and positive pointwise mutual information) in predicting participants RT and subjective judgments of semantic strength. Thus, we provide a computational alternative to computing semantic distance. Furthermore, this approach addresses key issues in cognitive theory, namely the breadth of the spreading activation process and the effect of semantic distance on memory retrieval. (PsycINFO Database Record

PMID: 28240936 [PubMed - indexed for MEDLINE]

Radiation Oncology Terminology Linker: A Step Towards a Linked Data Knowledge Base.

Stud Health Technol Inform. 2018;247:855-859

Authors: Lustberg T, van Soest J, Fick P, Fijten R, Hendriks T, Puts S, Dekker A

Abstract
Performing image feature extraction in radiation oncology is often dependent on the organ and tumor delineations provided by clinical staff. These delineation names are free text DICOM metadata fields resulting in undefined information, which requires effort to use in large-scale image feature extraction efforts. In this work we present a scale-able solution to overcome these naming convention challenges with a REST service using Semantic Web technology to convert this information to linked data. As a proof of concept an open source software is used to compute radiation oncology image features. The results of this work can be found in a public Bitbucket repository.

PMID: 29678082 [PubMed - in process]

Combining the Generic Entity-Attribute-Value Model and Terminological Models into a Common Ontology to Enable Data Integration and Decision Support.

Stud Health Technol Inform. 2018;247:541-545

Authors: Bouaud J, Guézennec G, Séroussi B

Abstract
The integration of clinical information models and termino-ontological models into a unique ontological framework is highly desirable for it facilitates data integration and management using the same formal mechanisms for both data concepts and information model components. This is particularly true for knowledge-based decision support tools that aim to take advantage of all facets of semantic web technologies in merging ontological reasoning, concept classification, and rule-based inferences. We present an ontology template that combines generic data model components with (parts of) existing termino-ontological resources. The approach is developed for the guideline-based decision support module on breast cancer management within the DESIREE European project. The approach is based on the entity attribute value model and could be extended to other domains.

PMID: 29678019 [PubMed - in process]

Exploring Semantic Data Federation to Enable Malaria Surveillance Queries.

Stud Health Technol Inform. 2018;247:6-10

Authors: Brenas JH, Al Manir MS, Zinszer K, Baker CJO, Shaban-Nejad A

Abstract
Malaria is an infectious disease affecting people across tropical countries. In order to devise efficient interventions, surveillance experts need to be able to answer increasingly complex queries integrating information coming from repositories distributed all over the globe. This, in turn, requires extraordinary coding abilities that cannot be expected from non-technical surveillance experts. In this paper, we present a deployment of Semantic Automated Discovery and Integration (SADI) Web services for the federation and querying of malaria data. More than 10 services were created to answer an example query requiring data coming from various sources. Our method assists surveillance experts in formulating their queries and gaining access to the answers they need.

PMID: 29677912 [PubMed - in process]

GGDonto ontology as a knowledge-base for genetic diseases and disorders of glycan metabolism and their causative genes.

J Biomed Semantics. 2018 Apr 18;9(1):14

Authors: Solovieva E, Shikanai T, Fujita N, Narimatsu H

Abstract
BACKGROUND: Inherited mutations in glyco-related genes can affect the biosynthesis and degradation of glycans and result in severe genetic diseases and disorders. The Glyco-Disease Genes Database (GDGDB), which provides information about these diseases and disorders as well as their causative genes, has been developed by the Research Center for Medical Glycoscience (RCMG) and released in April 2010. GDGDB currently provides information on about 80 genetic diseases and disorders caused by single-gene mutations in glyco-related genes. Many biomedical resources provide information about genetic disorders and genes involved in their pathogenesis, but resources focused on genetic disorders known to be related to glycan metabolism are lacking. With the aim of providing more comprehensive knowledge on genetic diseases and disorders of glycan biosynthesis and degradation, we enriched the content of the GDGDB database and improved the methods for data representation.
RESULTS: We developed the Genetic Glyco-Diseases Ontology (GGDonto) and a RDF/SPARQL-based user interface using Semantic Web technologies. In particular, we represented the GGDonto content using Semantic Web languages, such as RDF, RDFS, SKOS, and OWL, and created an interactive user interface based on SPARQL queries. This user interface provides features to browse the hierarchy of the ontology, view detailed information on diseases and related genes, and find relevant background information. Moreover, it provides the ability to filter and search information by faceted and keyword searches.
CONCLUSIONS: Focused on the molecular etiology, pathogenesis, and clinical manifestations of genetic diseases and disorders of glycan metabolism and developed as a knowledge-base for this scientific field, GGDonto provides comprehensive information on various topics, including links to aid the integration with other scientific resources. The availability and accessibility of this knowledge will help users better understand how genetic defects impact the metabolism of glycans as well as how this impaired metabolism affects various biological functions and human health. In this way, GGDonto will be useful in fields related to glycoscience, including cell biology, biotechnology, and biomedical, and pharmaceutical research.

PMID: 29669592 [PubMed - in process]

BiOnIC: A Catalog of User Interactions with Biomedical Ontologies.

Semant Web ISWC. 2017 Oct;10588:130-138

Authors: Kamdar MR, Walk S, Tudorache T, Musen MA

Abstract
BiOnIC is a catalog of aggregated statistics of user clicks, queries, and reuse counts for access to over 200 biomedical ontologies. BiOnIC also provides anonymized sequences of classes accessed by users over a period of four years. To generate the statistics, we processed the access logs of BioPortal, a large open biomedical ontology repository. We publish the BiOnIC data using DCAT and SKOS metadata standards. The BiOnIC catalog has a wide range of applicability, which we demonstrate through its use in three different types of applications. To our knowledge, this type of interaction data stemming from a real-world, large-scale application has not been published before. We expect that the catalog will become an important resource for researchers and developers in the Semantic Web community by providing novel insights into how ontologies are explored, queried and reused. The BiOnIC catalog may ultimately assist in the more informed development of intelligent user interfaces for semantic resources through interface customization, prediction of user browsing and querying behavior, and ontology summarization. The BiOnIC catalog is available at: http://onto-apps.stanford.edu/bionic.

PMID: 29637199 [PubMed]

Portuguese Norms of Name Agreement, Concept Familiarity, Subjective Frequency and Visual Complexity for 150 Colored and Tridimensional Pictures.

Span J Psychol. 2018 Apr 10;21:E8

Authors: Soares AP, Pureza R, Comesaña M

Abstract
Pictures are complex stimuli that require a careful control of several characteristics and attributes standardized for different languages. In this work we present for the first time European Portuguese (EP) norms for name agreement, concept familiarity, subjective frequency and visual complexity for a new set of 150 colored pictures. These pictures were selected to represent exemplars of the most used semantic categories in research and to depict objects which, though familiar to the participants, were rarely used in daily life, which makes them particularly prone to speech failures such as tip-of-the-tongue (TOT) states. Norms were collected from 640 EP native speakers that rated each picture in the four variables through a web-survey procedure. Results showed, as expected, that a large number of pictures in the dataset elicited a TOT response, and additionally that the ratings obtained in each of the dimensions are in line with those observed in other pictorial datasets. Norms can be freely downloaded at https://www.psi.uminho.pt/en/Research/Psycholinguistics/Pages/Databases.aspx.

PMID: 29633684 [PubMed - in process]

OC-2-KB: A software pipeline to build an evidence-based obesity and cancer knowledge base.

Proceedings (IEEE Int Conf Bioinformatics Biomed). 2017 Nov;2017:1284-1287

Authors: Lossio-Ventura JA, Hogan W, Modave F, Guo Y, He Z, Hicks A, Bian J

Abstract
Obesity has been linked to several types of cancer. Access to adequate health information activates people's participation in managing their own health, which ultimately improves their health outcomes. Nevertheless, the existing online information about the relationship between obesity and cancer is heterogeneous and poorly organized. A formal knowledge representation can help better organize and deliver quality health information. Currently, there are several efforts in the biomedical domain to convert unstructured data to structured data and store them in Semantic Web knowledge bases (KB). In this demo paper, we present, OC-2-KB (Obesity and Cancer to Knowledge Base), a system that is tailored to guide the automatic KB construction for managing obesity and cancer knowledge from free-text scientific literature (i.e., PubMed abstracts) in a systematic way. OC-2-KB has two important modules which perform the acquisition of entities and the extraction then classification of relationships among these entities. We tested the OC-2-KB system on a data set with 23 manually annotated obesity and cancer PubMed abstracts and created a preliminary KB with 765 triples. We conducted a preliminary evaluation on this sample of triples and reported our evaluation results.

PMID: 29629236 [PubMed]

Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations.

Bioinformatics. 2017 Aug 01;33(15):2337-2344

Authors: Zong N, Kim H, Ngo V, Harismendy O

Abstract
Motivation: A heterogeneous network topology possessing abundant interactions between biomedical entities has yet to be utilized in similarity-based methods for predicting drug-target associations based on the array of varying features of drugs and their targets. Deep learning reveals features of vertices of a large network that can be adapted in accommodating the similarity-based solutions to provide a flexible method of drug-target prediction.
Results: We propose a similarity-based drug-target prediction method that enhances existing association discovery methods by using a topology-based similarity measure. DeepWalk, a deep learning method, is adopted in this study to calculate the similarities within Linked Tripartite Network (LTN), a heterogeneous network generated from biomedical linked datasets. This proposed method shows promising results for drug-target association prediction: 98.96% AUC ROC score with a 10-fold cross-validation and 99.25% AUC ROC score with a Monte Carlo cross-validation with LTN. By utilizing DeepWalk, we demonstrate that: (i) this method outperforms other existing topology-based similarity computation methods, (ii) the performance is better for tripartite than with bipartite networks and (iii) the measure of similarity using network topology outperforms the ones derived from chemical structure (drugs) or genomic sequence (targets). Our proposed methodology proves to be capable of providing a promising solution for drug-target prediction based on topological similarity with a heterogeneous network, and may be readily re-purposed and adapted in the existing of similarity-based methodologies.
Availability and Implementation: The proposed method has been developed in JAVA and it is available, along with the data at the following URL: https://github.com/zongnansu1982/drug-target-prediction .
Contact: nazong@ucsd.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.

PMID: 28430977 [PubMed - indexed for MEDLINE]

From global action against malaria to local issues: state of the art and perspectives of web platforms dealing with malaria information.

Malar J. 2018 Mar 21;17(1):122

Authors: Briand D, Roux E, Desconnets JC, Gervet C, Barcellos C

Abstract
BACKGROUND: Since prehistory to present times and despite a rough combat against it, malaria remains a concern for human beings. While evolutions of science and technology through times allowed for some infectious diseases eradication in the 20th century, malaria resists.
OBJECTIVES: This review aims at assessing how Internet and web technologies are used in fighting malaria. Precisely, how do malaria fighting actors profit from these developments, how do they deal with ensuing phenomena, such as the increase of data volume, and did these technologies bring new opportunities for fighting malaria?
METHODS: Eleven web platforms linked to spatio-temporal malaria information are reviewed, focusing on data, metadata, web services and categories of users.
RESULTS: Though the web platforms are highly heterogeneous the review reveals that the latest advances in web technologies are underused. Information are rarely updated dynamically, metadata catalogues are absent, web services are more and more used, but rarely standardized, and websites are mainly dedicated to scientific communities, essentially researchers.
CONCLUSION: Improvement of systems interoperability, through standardization, is an opportunity to be seized in order to allow real time information exchange and online multisource data analysis. To facilitate multidisciplinary/multiscale studies, the web of linked data and the semantic web innovations can be used in order to formalize the different view points of actors involved in the combat against malaria. By doing so, new malaria fighting strategies could take place, to tackle the bottlenecks listed in the United Nation Millennium Development Goals reports, but also specific issues highlighted by the World Health Organization such as malaria elimination in international borders.

PMID: 29562918 [PubMed - in process]

The use of the term "radiosensitivity" through history of radiation: from clarity to confusion.

Int J Radiat Biol. 2018 Mar 13;:1-31

Authors: Britel M, Bourguignon M, Foray N

Abstract
PURPOSES: The term "radiosensitivity" appeared for the first time at the beginning of the 20th century, few years after the discovery of X-rays. Initially used by French and German radiologists, it illustrated the risk of radiation-induced (RI) skin reactions. From the 1950's, "radiosensitivity" was progressively found to describe other features of RI response such as RI cancers or cataracts. To date, such confusion may raise legal issues and complexify the message addressed to general public. Here, through an historical review, we aimed to better understand how this confusion appeared.
METHODS: To support our historical review, a quantitative and qualitative wording analysis of the "radiosensitivity" occurrences and its derived terms was performed with Google books, Pubmed, Web of Science™ databases and in all the ICRP publications.
CONCLUSIONS: While "radiosensitivity" was historically related to RI adverse tissue events attributable to cell death, the first efforts to quantify the RI risk specific to each organ/tissue revealed some different semantic fields that are not necessarily compatible together (e.g. adverse tissue events for skin, cataracts for eyes, RI cancer for breast or thyroid). To avoid such confusion, we propose to keep the historical definition of "radiosensitivity" to any clinical and cellular consequences of radiation attributable to cell death and to introduce the term "radiosusceptibility" to describe the RI cancers or any feature that is attributable to cell transformation.

PMID: 29533136 [PubMed - as supplied by publisher]

Disease Compass- a navigation system for disease knowledge based on ontology and linked data techniques.

J Biomed Semantics. 2017 Jun 19;8(1):22

Authors: Kozaki K, Yamagata Y, Mizoguchi R, Imai T, Ohe K

Abstract
BACKGROUND: Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated mechanisms of diseases are related to concepts across various medical domains. The authors developed a River Flow Model (RFM) of diseases, which captures diseases as the causal chains of abnormal states. It represents causes of diseases, disease progression, and downstream consequences of diseases, which is compliant with the intuition of medical experts. In this paper, we discuss a fact repository for causal chains of disease based on the disease ontology. It could be a valuable knowledge base for advanced medical information systems.
METHODS: We developed the fact repository for causal chains of diseases based on our disease ontology and abnormality ontology. This section summarizes these two ontologies. It is developed as linked data so that information scientists can access it using SPARQL queries through an Resource Description Framework (RDF) model for causal chain of diseases.
RESULTS: We designed the RDF model as an implementation of the RFM for the fact repository based on the ontological definitions of the RFM. 1554 diseases and 7080 abnormal states in six major clinical areas, which are extracted from the disease ontology, are published as linked data (RDF) with SPARQL endpoint (accessible API). Furthermore, the authors developed Disease Compass, a navigation system for disease knowledge. Disease Compass can browse the causal chains of a disease and obtain related information, including abnormal states, through two web services that provide general information from linked data, such as DBpedia, and 3D anatomical images.
CONCLUSIONS: Disease Compass can provide a complete picture of disease-associated processes in such a way that fits with a clinician's understanding of diseases. Therefore, it supports user exploration of disease knowledge with access to pertinent information from a variety of sources.

PMID: 28629436 [PubMed - indexed for MEDLINE]