Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology

Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization

Now a day, health information management and utilization is the demanding task to health informaticians for delivering the eminence healthcare services. Extracting the similar cases from the case database can aid the doctors to recognize the same kind of patients and their treatment details. Accordingly, this paper introduces the method called H-BCF for retrieving the similar cases from the case database. Initially, the patient’s case database is constructed with details of different patients and their treatment details. If the new patient comes for treatment, then the doctor collects the information about that patient and sends the query to the H-BCF. The H-BCF system matches the input query with the patient’s case database and retrieves the similar cases. Here, the PSO algorithm is used with the BCF for retrieving the most similar cases from the patient’s case database. Finally, the Doctor gives treatment to the new patient based on the retrieved cases. The performance of the proposed method is analyzed with the existing methods, such as PESM, FBSO-neural network, and Hybrid model for the performance measures accuracy and F-Measure. The experimental results show that the proposed method attains the higher accuracy of 99.5% and the maximum F-Measure of 99% when compared to the existing methods

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score

Ontologies Applied in Clinical Decision Support System Rules:Systematic Review

BackgroundClinical decision support systems (CDSSs) are important for the quality and safety of health care delivery. Although CDSS rules guide CDSS behavior, they are not routinely shared and reused. ObjectiveOntologies have the potential to promote the reuse of CDSS rules. Therefore, we systematically screened the literature to elaborate on the current status of ontologies applied in CDSS rules, such as rule management, which uses captured CDSS rule usage data and user feedback data to tailor CDSS services to be more accurate, and maintenance, which updates CDSS rules. Through this systematic literature review, we aim to identify the frontiers of ontologies used in CDSS rules. MethodsThe literature search was focused on the intersection of ontologies; clinical decision support; and rules in PubMed, the Association for Computing Machinery (ACM) Digital Library, and the Nursing & Allied Health Database. Grounded theory and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines were followed. One author initiated the screening and literature review, while 2 authors validated the processes and results independently. The inclusion and exclusion criteria were developed and refined iteratively. ResultsCDSSs were primarily used to manage chronic conditions, alerts for medication prescriptions, reminders for immunizations and preventive services, diagnoses, and treatment recommendations among 81 included publications. The CDSS rules were presented in Semantic Web Rule Language, Jess, or Jena formats. Despite the fact that ontologies have been used to provide medical knowledge, CDSS rules, and terminologies, they have not been used in CDSS rule management or to facilitate the reuse of CDSS rules. ConclusionsOntologies have been used to organize and represent medical knowledge, controlled vocabularies, and the content of CDSS rules. So far, there has been little reuse of CDSS rules. More work is needed to improve the reusability and interoperability of CDSS rules. This review identified and described the ontologies that, despite their limitations, enable Semantic Web technologies and their applications in CDSS rules

Group decision making and quality-of-information in e-Health systems

Knowledge is central to the modern economy and society. Indeed, the knowledge society has transformed the concept of knowledge and is more and more aware of the need to overcome the lack of knowledge when has to make options or address its problems and dilemmas. One`s knowledge is less based on exact facts and more on hypotheses, perceptions or indications. Even when we use new computational artefacts and novel methodologies for problem solving, like the use of Group Decision Support Systems (GDSS), the question of incomplete information is in most of the situations marginalized. On the other hand, common sense tells us that when a decision is made it is impossible to have a perception of all the information involved and the nature of its intrinsic quality. Therefore, something has to be made in terms of the information available and the process of its evaluation. It is under this framework that a Multi-valued Extended Logic Programming language will be used for knowledge representation and reasoning, leading to a model that embodies the Quality-of-Information (QoI) and its quantification, along the several stages of the decision making process. In this way it is possible to provide a measure of the value of the QoI that supports the decision itself. This model will be here presented in the context of a GDSS for VirtualECare, a system aimed at sustaining online healthcare services

ADAPT: Approach to Develop context-Aware solutions for Personalised asthma managemenT

People with asthma have heterogeneous triggers and symptoms, which they need to be aware of in order to implement the strategies to manage their condition. Context-aware reasoning has the potential to provide the personalisation that is required to address the heterogeneity of asthma by helping people to define the information that is relevant considering the characteristics of their condition and delivering services based on this information. This research work proposes the Approach to Develop context-Aware solutions for Personalised asthma managemenT (ADAPT), whose aim is to facilitate the creation of solutions allowing the required customisation to address the heterogeneity of asthma. ADAPT is the result of the constant interaction with people affected by asthma throughout the research project, which was possible to achieve thanks to the collaboration formed with the Centre for Applied Research of Asthma UK. ADAPT context dimensions facilitate the development of preventive and reactive features that can be configured depending on the characteristics of the person with asthma. The approach also provides support to people not knowing their triggers through case-based reasoning and includes virtual assistant as a complementing technology supporting asthma management. ADAPT is validated by people with asthma, carers and experts in respiratory conditions, who evaluated a mobile application that was built based on the approach

Decision support systems for adoption in dental clinics: a survey

While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic

Hybrid Multi-Agents and Case Based Reasoning for Aiding Green Practice in Institutions of Higher Learning

Sustainability is a concern that has been raised in many domains especially in institutions of higher learning such as universities. Hence, universities are implementing Green practices to promote sustainability. Similarly Green practice implementation in universities for attaining sustainability has been the priority for most universities across the world, mainly in ensuring the effectiveness and efficiency of Information Technology (IT) related service. Over the years, a few approaches have been developed to facilitate Green practice in institutions of higher learning, however these approaches are not autonomous and do not provide adequate information on Green implementation initiatives. Moreover, institutions of higher learning utilize manual checklist assessment questionnaire to evaluate their current Green practice. Therefore, this study proposes a system model that integrates hybrid multi-agent and Case Based Reasoning (CBR). The CBR technique facilitates Green implementation by providing information on how institution of higher learning can adopt Green practices initiative, whereas software agents autonomously assess the current Green practice initiative implemented in institutions of higher learning. Findings from this paper show how the hybrid multi-agent and CBR aid universities implement Green practice for sustainability attainment in institutions of higher learning