ОНТОЛОГО-КЕРОВАНІ ІНФОРМАЦІЙНІ СИСТЕМИ В ЗАБЕЗПЕЧЕННІ БЕЗПЕРЕРВНОГО ПРОФЕСІЙНОГО РОЗВИТКУ ЛІКАРІВ ТА ПРОВІЗОРІВ

The article is devoted to the issue of medical education quality assurance, the improvement of the medical staff qualifications and the continuous professional development of doctors and pharmacists using modern information and communication technologies. The advantages of using the ontological approach to knowledge representation in the process of creation and use of medical information systems in order to provide access to the maximum amount of information through the interoperable integration and aggregation of knowledge-oriented resources and information sources are determined. A brief analysis of existing medical ontologies (from collections of highly specialized thematic ontologies to national terminology health systems), which revealed a number of disadvantages and obstacles to their use in the system of modern medical education in Ukraine is presented. The necessity of creation of a unified interoperable environment with existing ontological solutions by means of unified user interface for increasing the efficiency of continuous professional development of doctors and pharmacists is determined. The main aspects of the development of ontology-driven information systems, which can be used for the medical information systems structure, functionality and interfaces design, but also in the process of using them to solve practical problems, are outlined. The experience of using IT-TODOS cognitive software and information tools that provide interactive temporal and semantic synchronization of information resources, regardless of the format, standards and technologies of their creation, for the design and use of ontology-driven medical information systems is presented. Its functionality and the advantages of using in the process of ensuring the continuous professional development of doctors and pharmacists are described.Стаття присвячена питанню забезпечення якості медичної освіти, підвищення кваліфікації медичних кадрів і безперервного професійного розвитку лікарів та провізорів засобами сучасних інформаційно-комунікаційних технологій. Визна­чені переваги застосування онтологічного підходу до представлення знань у процесі створення і використання медичних інформаційних систем з метою надання доступу до максимально повного обсягу інформації шляхом інтероперабельної інтеграції та агрегації знання-орієнтованих ресурсів та інформаційних джерел. Наведений короткий аналіз існуючих онтологій медичного призначення (від колекцій вузькоспеціалізованих тематичних онтологій до національних термінологічних систем охорони здоров’я), що виявив низку недоліків та перешкод їх використання в системі сучасної медичної освіти в Україні. Визначена необхідність створення єдиного середовища інтероперабельної взаємодії з існуючими онтологічними рішеннями засобами уніфікованого інтерфейсу користувача для підвищення ефективності безперервного професійного розвитку лікарів та провізорів. Викладені основні аспекти розробки онтолого-керованих інформаційних систем, що можуть бути використані не лише при проектуванні і розробці структури, функціоналу та інтерфейсів медичних інформаційних систем, а й у процесі їх використання для вирішення практичних завдань. Представлений досвід застосування ІТ-ТОДОС, когнітивні програмно-інформаційні засоби якої забезпечують інтерактивну інтероперабельну темпоральну та семантичну синхронізацію інформаційних ресурсів незалежно від формату, стандартів і технологій їх створення, для розробки та використання онтолого-керованих медичних інформаційних систем, наведений її функціонал та переваги використання в процесі забезпечення безперервного професійного розвитку лікарів та провізорів

Disease Ontology: improving and unifying disease annotations across species.

Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD\u27s and RGD\u27s disease term annotations identified new terms that enhance DO\u27s representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO\u27s domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO\u27s usability across human data, MGD, RGD and the rest of the model organism database community. Dis Model Mech 2018 Mar 12;11(3):dmm032839

An Effective Method to Measure Disease Similarity Using Gene and Phenotype Associations

Motivation: In order to create controlled vocabularies for shared use in different biomedical domains, a large number of biomedical ontologies such as Disease Ontology (DO) and Human Phenotype Ontology (HPO), etc., are created in the bioinformatics community. Quantitative measures of the associations among diseases could help researchers gain a deep insight of human diseases, since similar diseases are usually caused by similar molecular origins or have similar phenotypes, which is beneficial to reveal the common attributes of diseases and improve the corresponding diagnoses and treatment plans. Some previous are proposed to measure the disease similarity using a particular biomedical ontology during the past few years, but for a newly discovered disease or a disease with few related genetic information in Disease Ontology (i.e., a disease with less disease-gene associations), these previous approaches usually ignores the joint computation of disease similarity by integrating gene and phenotype associations.Results: In this paper we propose a novel method called GPSim to effectively deduce the semantic similarity of diseases. In particular, GPSim calculates the similarity by jointly utilizing gene, disease and phenotype associations extracted from multiple biomedical ontologies and databases. We also explore the phenotypic factors such as the depth of HPO terms and the number of phenotypic associations that affect the evaluation performance. A final experimental evaluation is carried out to evaluate the performance of GPSim and shows its advantages over previous approaches

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

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