Ontology-based Decision Support System Research Articles

An Ontology-Based Decision Support System for Tailored Clinical Nutrition Recommendations for Patients With Chronic Obstructive Pulmonary Disease: Development and Acceptability Study.

Chronic obstructive pulmonary disease (COPD) is a chronic condition among the main causes of morbidity and mortality worldwide, representing a burden on health care systems. Scientific literature highlights that nutrition is pivotal in respiratory inflammatory processes connected to COPD, including exacerbations. Patients with COPD have an increased risk of developing nutrition-related comorbidities, such as diabetes, cardiovascular diseases, and malnutrition. Moreover, these patients often manifest sarcopenia and cachexia. Therefore, an adequate nutritional assessment and therapy are essential to help individuals with COPD in managing the progress of the disease. However, the role of nutrition in pulmonary rehabilitation (PR) programs is often underestimated due to a lack of resources and dedicated services, mostly because pneumologists may lack the specialized training for such a discipline. This work proposes a novel knowledge-based decision support system to support pneumologists in considering nutritional aspects in PR. The system provides clinicians with patient-tailored dietary recommendations leveraging expert knowledge. The expert knowledge-acquired from experts and clinical literature-was formalized in domain ontologies and rules, which were developed leveraging the support of Italian clinicians with expertise in the rehabilitation of patients with COPD. Thus, by following an agile ontology engineering methodology, the relevant formal ontologies were developed to act as a backbone for an application targeted at pneumologists. The recommendations provided by the decision support system were validated by a group of nutrition experts, whereas the acceptability of such an application in the context of PR was evaluated by pneumologists. A total of 7 dieticians (mean age 46.60, SD 13.35 years) were interviewed to assess their level of agreement with the decision support system's recommendations by evaluating 5 patients' health conditions. The preliminary results indicate that the system performed more than adequately (with an overall average score of 4.23, SD 0.52 out of 5 points), providing meaningful and safe recommendations in compliance with clinical practice. With regard to the acceptability of the system by lung specialists (mean age 44.71, SD 11.94 years), the usefulness and relevance of the proposed solution were extremely positive-the scores on each of the perceived usefulness subscales of the technology acceptance model 3 were 4.86 (SD 0.38) out of 5 points, whereas the score on the intention to use subscale was 4.14 (SD 0.38) out of 5 points. Although designed for the Italian clinical context, the proposed system can be adapted for any other national clinical context by modifying the domain ontologies, thus providing a multidisciplinary approach to the management of patients with COPD.

Selection of qualified reviewer and avoiding conflicts of interest within the issue of compromised peer review in paper retractions using an ontology-based decision support system

Selection of qualified reviewer and avoiding conflicts of interest within the issue of compromised peer review in paper retractions using an ontology-based decision support system

АНАЛІЗ ТРИБОЛОГІЧНИХ НАНОМАТЕРІАЛІВ ДЛЯ ПІДВИЩЕННЯ ЗНОСОСТІЙКОСТІ І ДОВГОВІЧНОСТІ МЕХАНІЧНИХ КОНТАКТУЮЧИХ ПОВЕРХОНЬ ДЕТАЛЕЙ МАШИН І МЕХАНІЗМІВ НА ОСНОВІ ОНТОЛОГІЧНОЇ СИСТЕМИ ПІДТРИМКИ ПРИЙНЯТТЯ РІШЕНЬ

The object of research is tribological nanomaterials (TrNMs), which are used to improve the qualities of the mechanical contacting surfaces of mechanical components. The goal is to create Decision Support Systems (DSS) based on the ontological system when choosing the TrNM. It was carried out an analysis of existing studies and publications as to the use of TrNM, which are available on the market of Ukraine and European countries. The analysis showed that TrNMs are classified according to the friction surfaces main features and their purpose. It has been determined that the use of TrNM shows a significant technical and economic effect, as the product service life extents and also fuel consumption decreases due to the reduction of the friction coefficient. It was found that an important consideration when choosing that or another TrNM should be their sustainability, in terms of their environmentally friendly manufacturing and use on mechanical components. It is noted that there is a problem of the existing TrNM classifiers and their substantiating, since their formation lags behind the dynamics of scientific and technological progress. It was reasoned expediency of using an Ontology-Based Decision Support Systems (OBDSS) regarding the selection of TrNM for improving the performances of the mechanical components contacting surfaces. The following results were obtained. According to the analysis of the current interest modern TrNMs and their characteristics, the technologies for the TNMs applying have been considered: "KESON" with the filling of on the friction surfaces micro-valleys with the formation of anti-friction, wear-resistant and adhesive films (coatings), as well as technologies for the use of metal conditioners "Fenom", "ER", "SR3" and "Militec-1" additives, which extend the product service life without overhauls, and remetallizing agents "Lubrifilm" and "Megaforce" with their effect of "non-wear-out-ability". It was found that the main disadvantage of such TrNMs is a pollution of the environment, namely of drinking, ground and surface water. The solution to the environmental issue is possible due to the use of nanodiamonds as a specific universal nanocarbon TrNM, which has the properties of decomposition of harmful organic chemicals. Their “representatives” are "Lubrifilm Diamond Run Ln", "Nanodiamond Green Run", nano additives "Fenom Nanodiamond Green Run" (based on nanodiamonds), "Fenom Old Chap" (based on bentonite nanoparticles), "Renom Engine Nanoguard" (for motor oils) and "Renom Gear Nanoguard" (for transmission oils). To facilitate the choice of TrNM for improving the performances of the mechanical components contacting surfaces, the OBDSS is proposed. Conclusions. The scientific novelty of the obtained results is as follows: it has been created the OBDSS, which takes into account the peculiarities and characteristics of TrNM according to the customer needs, which able to narrow the search range and reduce the time for TrNM selection. The presented materials can be useful when conducting the theoretical and experimental studies on the mechanical components service life in the field of mechanical engineering.

Selection of qualified reviewers and avoiding conflicts of interest within the issue of compromised peer review in paper retractions using an ontology-based decision support system

Selection of qualified reviewers and avoiding conflicts of interest within the issue of compromised peer review in paper retractions using an ontology-based decision support system

Semantic sensor network ontology based decision support system for forest fire management

The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for various applications in industries such as medical, environmental monitoring, home automation, etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and the converted dataset into Resource Description Framework (RDF). This research describes the various steps in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation and supports reasoning and the general semantics of the open world, followed by all the ontologies.

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study.

BackgroundDue to an increase in life expectancy, the prevalence of chronic diseases is also on the rise. Clinical practice guidelines (CPGs) provide recommendations for suitable interventions regarding different chronic diseases, but a deficiency in the implementation of these CPGs has been identified. The PITeS-TiiSS (Telemedicine and eHealth Innovation Platform: Information Communications Technology for Research and Information Challenges in Health Services) tool, a personalized ontology-based clinical decision support system (CDSS), aims to reduce variability, prevent errors, and consider interactions between different CPG recommendations, among other benefits.ObjectiveThe aim of this study is to design, develop, and validate an ontology-based CDSS that provides personalized recommendations related to drug prescription. The target population is older adult patients with chronic diseases and polypharmacy, and the goal is to reduce complications related to these types of conditions while offering integrated care.MethodsA study scenario about atrial fibrillation and treatment with anticoagulants was selected to validate the tool. After this, a series of knowledge sources were identified, including CPGs, PROFUND index, LESS/CHRON criteria, and STOPP/START criteria, to extract the information. Modeling was carried out using an ontology, and mapping was done with Health Level 7 Fast Healthcare Interoperability Resources (HL7 FHIR) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT; International Health Terminology Standards Development Organisation). Once the CDSS was developed, validation was carried out by using a retrospective case study.ResultsThis project was funded in January 2015 and approved by the Virgen del Rocio University Hospital ethics committee on November 24, 2015. Two different tasks were carried out to test the functioning of the tool. First, retrospective data from a real patient who met the inclusion criteria were used. Second, the analysis of an adoption model was performed through the study of the requirements and characteristics that a CDSS must meet in order to be well accepted and used by health professionals. The results are favorable and allow the proposed research to continue to the next phase.ConclusionsAn ontology-based CDSS was successfully designed, developed, and validated. However, in future work, validation in a real environment should be performed to ensure the tool is usable and reliable.

ECOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.

Collaborative Ontology Engineering Methodologies for the Development of Decision Support Systems: Case Studies in the Healthcare Domain

New models and technological advances are driving the digital transformation of healthcare systems. Ontologies and Semantic Web have been recognized among the most valuable solutions to manage the massive, various, and complex healthcare data deriving from different sources, thus acting as backbones for ontology-based Decision Support Systems (DSSs). Several contributions in the literature propose Ontology engineering methodologies (OEMs) to assist the formalization and development of ontologies, by providing guidelines on tasks, activities, and stakeholders’ participation. Nevertheless, existing OEMs differ widely according to their approach, and often lack of sufficient details to support ontology engineers. This paper performs a meta-review of the main criteria adopted for assessing OEMs, and major issues and shortcomings identified in existing methodologies. The key issues requiring specific attention (i.e., the delivery of a feasibility study, the introduction of project management processes, the support for reuse, and the involvement of stakeholders) are then explored into three use cases of semantic-based DSS in health-related fields. Results contribute to the literature on OEMs by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting DSS.

An Ontology-based Decision Support System for Multi-objective Prediction Tasks

An Ontology-based Decision Support System for Multi-objective Prediction Tasks

A decision support system for DM algorithm selection based on module extraction

Data mining techniques are needed in various fields. However, most data researchers do not have sufficient knowledge and experience. Due to the significant number of algorithms and parameters for data analysis, intuition-based decisions can’t lead to optimal solutions. Ontology is widely adopted to build knowledge-driven decision support systems since it is suited to encapsulate the concepts and relationships of terms associated with various domains. It is suitable for capturing knowledge by computers, allowing sharing and reusing it whenever necessary. All concepts and relationships about data analysis can be described using Web Ontology Language (OWL). Its reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend suitable solutions for a specific analysis task by considering the data characteristics and task requirements. In the previous work, we have developed a system that describes comprehensive knowledge and logical internal relationships about data mining. We found that the reasoning and query complexity is high in the large size ontology, so this paper focuses on the modeling and implementation of an ontology-based decision support system for data mining algorithm selection that contains a new sub-ontology extraction method. Extracting effective content can significantly reduce the size of the ontology. This extraction method can improve query efficiency on the premise of ensuring the quality of information.

RETRACTED ARTICLE: Ontology-based prediction of cochlear implantation outcome using cross-modal plasticity analysis

Cochlear implantation is a surgical procedure by which an electronic medical device, namely a cochlear implant, is fitted to the individual who has the challenge of hearing. It takes the function of the damaged inner ear, the cochlea. The hearing impaired may not benefit from this procedure because of cross-modal plasticity. This work aims to study the implantee’s visual modal adaptation by analyzing the visual evoked potential. The proposed methodology analyses the existence of cross-modal reorganization in the auditory cortex of bilateral prelingually deaf children after cochlear implantation using visual evoked potential. Fifty healthy, 50 prelingually, deaf children 50 cochlear implanted were considered as a cohort of the Visual evoked potential. The evoked potential recorded using pattern reversal stimulation. The amplitude and latency of N75, P100, and N145 components show a significant difference in normal, cochlear, and deaf. The early diagnosis of hearing impairment demands the patients and doctors to make a series of decisions for the betterment of the implantee in an accelerated manner through traditional database methodology implemented for making decisions, analyzing, interpreting, processing of data is so difficult in the conventional system. Medical knowledge represented for the computers to analyze the inferred data and to make the decisions. Ontology is the most potent tool to encode medical data semantically. A fuzzy ontology-based decision support system built to predict the cochlear implantation outcome. The ontology created using Protege software tool and the decision taken using Jena and pellet reasoner. A fuzzy-based prediction model designed using a fuzzy interface system to estimate the categories of auditory performance (CAP) test reliable indicators of cochlear implantation success.

An Ontology-Based Personalized Decision Support System for Use in the Complex Chronically Ill Patient.

Management of the complex chronically ill patient is highly problematic. This is due to the need to complement recommendations in clinical guidelines with recommendations based on treatments performed on a representative set of patients. It is necessary to identify conflicts between the recommendations of different guidelines designed for handling specific chronic diseases. The PITeS-TIiSS project (code PI15/01213) aims to overcome this problem by designing and deploying an ontology-based personalized clinical decision support tool. This helps to improve personalized decisions and reduces variability in clinical practice in an integrated care environment. This paper describes the methodology followed in developing the ontology used to infer clinical knowledge and to develop and implement the support tool. The tool will access the information provided by the Electronic Health Record of Andalusian Health Public Service, the main healthcare provider in a region in Spain with more than eight million inhabitants.

Recommendation System for Complementary Breastfeeding using Ontology Modelling and Naïve Bayes

Complementary breastfeeding is an additional food given to the baby started from six to 24 months. The giving of complementary foods is given gradually according to the age of the children and adapted to the condition of the children, such as allergies suffered or malnutrition suffered. This research aims to develop an ontology based decision support system that will help mothers in giving breastfeeding to their babies with keep regarding to their food preferences. This research successfully develops a content-based recommendation system by performing the Naïve Bayes Method and modified the existing ontology modelling and also develop a mobile application with the Android platform so that it can be more accessible to many people. Thirty-five users evaluated the system, and the result of the usability testing shows that the user satisfaction level using SUS (System Scale Usability) method is 79.57, which is in Grade A-. This grade indicates that the system can be well understood by the user and can help mothers in choosing breastfeeding recipes or menus.

Enhancing supply chain resilience using ontology-based decision support system

ABSTRACTToday’s scenario of manufacturing and supply chain is full of uncertainty because of numerous types of disruptions and failures such as fire, storm, machine failure, are a few names. Always supply chain disruptions present disastrous impacts, although probabilities of happening are low. In the recent robust as well as flexible supply chain has captured the focus of researchers in designing a supply chain network with consideration of disruption’s risk. In this work, an ontology-based decision support system is proposed to intensify the supply chain resilience during a disruption. The concept of semantic and ontology is adopted in developing the knowledge base for the entire supply chain network including manufacturing units. Protégé is used for defining the classes and sub-classes along with numerous types of properties and expressed in a rule-based system using semantic web rule language (SWRL). Furthermore, a mixed integer linear programming model with an objective of maximising quantified resilience to fulfil the demand. A hybrid particle swarm optimisation – differential evolution (PSO-DE) is utilised as an optimisation technique for the defined problem. In performing the study, a set of simulated data is formed and then interpreted in the ontology for an optimal selection of recovery activity.

An Ontology-Based Decision Support System for the Diagnosis of Plant Diseases

There are several cities and countries whose population depends on agriculture. Crops demand close monitoring regarding diseases because these ones can affect significantly both production and post-harvest life. The identification of disease symptoms plays a crucial role in the successful cultivation of crops. The diagnosis of diseases is a challenging task since many symptoms should be considered, which makes a proper diagnosis becomes a knowledge handling problem. This paper specifies an ontology-based decision support system that promotes the knowledge of experts for the plant disease diagnosis to farmers. This system takes advantage of ontologies in two ways, to exploit the knowledge contained in the ontology for decision support purposes, in this case, the diagnosis of diseases, and to provide a standard vocabulary for integrating phytopathology data sources. The system was evaluated for the diagnosis of diseases presented in short-cycle and perennial crops achieving promising results based on the F-measure metric.

Ontology-based Decision Support System with Analytic Hierarchy Process for Tour Package Selection

The popularity of Internet and growing B2C electronic commerce nowadays make product or service information easy to be acquired. However, making an optimal choice from the various alternative products becomes a laborious process. In this paper, an ontology-based Decision Support System (DSS) with Analytic Hierarchy Process (AHP) was proposed for the specific application of tour package selection. The system is composed of two subsystems, the product gatherer and the decision maker, which are used to find out right products and make an expected choice respectively. In the product gatherer subsystem, an ontology-based web service architecture with Web Ontology Language (OWL) was established for the semantic content processing of product information. The Simple Object Access Protocol (SOAP) is utilized to establish the communication interface and gather XML-based contents through Remote Procedure Calls (RPC) between the system and the database servers of travel agencies. In the decision maker subsystem, the Analytic Hierarchy Process is utilized to make an optimal decision for satisfying the requirement given by the consumer. The system aims to help consumers to avoid falling into decision-making hesitation and get an expected choice from various and similar products.

Towards an ontological infrastructure for chemical process simulation and optimization in the context of eco-industrial parks

In this paper, we introduce the concept of constructing an ontology-based decision support system called J-Park Simulator for the design and operation of eco-industrial parks. It is inspired by Jurong industrial park in Singapore. A biodiesel plant is implemented into the system as a first step. OntoCAPE is adapted for the purposes of the biodiesel plant to establish a knowledge base, which is employed to carry out a number of applications via J-Park Simulator. Firstly, information query can be performed. Information of the biodiesel plant can be extracted through natural language query. Secondly, J-Park Simulator can be used to carry out process simulation. New process equilibrium can be evaluated after certain operation parameters change. Thirdly, process optimization can be realized through J-Park Simulator. Optimal operation condition under different market scenarios can be obtained for the biodiesel producing process in order to reduce the energy consumption and achieve maximal plant profit.

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

IntroductionWe have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue. Material and methodsThe ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term “Pregnancy, Ectopic”) and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system. ResultsThe resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64–42.05]. DiscussionThe ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology.

An Ontology-based System for Building Individualized Education Plans for Students with Special Educational Needs

This paper presents the results of a two years research project aimed at adopting semantic web technology to draft the IEP (Individualized Education Plan) for pupils with special educational needs in school. It includes a report of lessons learned through the collaborative building of an ontology in a concrete and multidisciplinary context, as well as in developing an ontology-based decision support system.

OntoDiabetic: An Ontology-Based Clinical Decision Support System for Diabetic Patients

Clinical decision support systems (CDSS) assist medical practitioners in their daily work, thereby enhancing the quality of care given to a patient. It supports them in the decision-making process and suggests appropriate treatments. The use of the ontology to build knowledge-driven decision support systems is widely adopted. Ontology is best suited to encapsulate the concepts and relationships of terms associated with the medical domain. It is suitable for capturing medical knowledge in a formal way, allowing sharing and reusing it whenever necessary. All concepts and relationships detailed in clinical guidelines can be implemented using Web Ontology Language (OWL). The reasoning mechanism is vital in any knowledge-based system. Ontology can be reasoned to recommend the suitable treatment for a patient by considering the current medical status of the patient. OntoDiabetic, an ontology-based decision support system is developed to assess the risk factors and provide appropriate treatment suggestions for diabetic patients. This paper focuses on the modeling and implementation of clinical guidelines using OWL2 rules and the reasoning process of the OntoDiabetic system. The case study is conducted for patients having the risk of overt cardiovascular disease, diabetic nephropathy and hypertension in primary health centers of Oman.