Diabetes is a non-communicable disease that has reached epidemic proportions, affecting 537 million people globally. Artificial Intelligence can support patients or clinicians in diabetes nutrition therapy - the first medical therapy in most cases of Type 1 and Type 2 diabetes. In particular, ontology-based recommender and decision support systems can deliver a computable representation of experts\' knowledge, thus delivering patient-tailored nutritional recommendations or supporting clinical personnel in identifying the most suitable diet. This work proposes a systematic literature review of the domain ontologies describing diabetes in such systems, identifying their underlying conceptualizations, the users targeted by the systems, the type(s) of diabetes tackled, and the nutritional recommendations provided. This review also delves into the structure of the domain ontologies, highlighting several aspects that may hinder (or foster) their adoption in recommender and decision support systems for diabetes nutrition therapy. The results of this review process allow to underline how recommendations are formulated and the role of clinical experts in developing domain ontologies, outlining the research trends characterizing this research area. The results also allow for identifying research directions that can foster a preeminent role for clinical experts and clinical guidelines in a cooperative effort to make ontologies more interoperable - thus enabling them to play a significant role in the decision-making processes about diabetes nutrition therapy.

Timely management of Chronic Obstructive Pulmonary Disease (COPD) exacerbations can improve recovery and reduce the risk of hospitalization. Digital therapeutics are digital interventions, based on best evidence, designed to provide home-based, patient-centered and pervasive self-management support to patients. Digital therapeutics can be effectively used to offer personalized and explainable self-management and behaviour modification resources to patients to reduce the burden of COPD, especially the prevention of acute COPD exacerbations. The functionalities of COPD specific digital therapeutics for self-management need to be grounded in clinical evidence and behavioral theories, in keeping with the self-management needs of COPD patients and their care providers. In this paper, we report the functionalities of a COPD digital therapeutic mobile application based on a needs analysis qualitative study involving both COPD patients and physicians, and, based on the study\'s finding, we present a knowledge-driven digital therapeutic for COPD self-management.

Computer-based medical diagnosis expert systems are considered both accurate and educationally helpful in most cases. Dizziness and vertigo are among the most common complaints however E.N.T. surgeons and neuro-otologists are not available in the peripheral areas. Computer-Aided Diagnosis In NeurOtology (CADINO) can be of immense value for these unprivileged dizzy patients of remote and rural areas. The study aimed to document the strength, weaknesses and capabilities of CADINO in terms of accuracy, educational usefulness, functionality and effectiveness. Design Hospital-based observational study of a diagnostic tool. Settings Otorhinolaryngology Department of a tertiary care medical college hospital. This prospective study was conducted in 70 patients, 24 simulated cases and 6 case reports from journals. The study included even the feedback of the clinicians before and after consultation. Eleven ENT residents, 14 ENT surgeons [8 teachers and 6 consultants] participated in the study. The overall diagnostic accuracy of the CADINO was found 86%. While in the patients, CADINO accuracy was found 84% approximately similar to faculties/consultants (80%) but it was significantly better than that of residents (57%). Most of the clinicians (84%), rated the CADINO consultation as being educationally helpful, and useful for patient management. CADINO was found very effective and convenient as it could be operated in the OPD simultaneously while evaluating the dizzy patients. CADINO provided accurate diagnostic suggestions. It was found improving patient safety and quality of care by enhancing knowledge and cognitive skills of the clinicians.

UNASSIGNED: Tinnitus, known as \"ringing in the ears\", is a widespread and frequently disabling hearing disorder. No pharmacological treatment exists, but clinical management techniques, such as tinnitus retraining therapy (TRT), prove effective in helping patients. Although effective, TRT is not widely offered, due to scarcity of expertise and complexity because of a high level of personalization. Within this study, a data-driven clinical decision support tool is proposed to guide clinicians in the delivery of TRT.
UNASSIGNED: This research proposes the formulation of data analytics models, based on supervised machine learning (ML) techniques, such as classification models and decision rules for diagnosis, and action rules for treatment to support the delivery of TRT. A knowledge-based framework for clinical decision support system (CDSS) is proposed as a UI-based Java application with embedded WEKA predictive models and Java Expert System Shell (JESS) rule engine with a pattern-matching algorithm for inference (Rete). The knowledge base is evaluated by the accuracy, coverage, and explainability of diagnostics predictions and treatment recommendations.
UNASSIGNED: The ML methods were applied to a clinical dataset of tinnitus patients from the Tinnitus and Hyperacusis Center at Emory University School of Medicine, which describes 555 patients and 3,000 visits. The validated ML classification models for diagnosis and rules: association and actionable treatment patterns were embedded into the knowledge base of CDSS. The CDSS prototype was tested for accuracy and explainability of the decision support, with preliminary testing resulting in an average of 80% accuracy, satisfactory coverage, and explainability.
UNASSIGNED: The outcome is a validated prototype CDS system that is expected to facilitate the TRT practice.

Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems.
This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data.
The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine.
PROSPERO CRD42018106501.

Modern production systems can benefit greatly from integrated and up-to-date digital representations. Their applications range from consistency checks during the design phase to smart manufacturing to maintenance support. Such digital twins not only require data, information and knowledge as inputs but can also be considered integrated models themselves. This paper provides an overview of data, information and knowledge typically available throughout the lifecycle of production systems and the variety of applications driven by data analysis, expert knowledge and knowledge-based systems. On this basis, we describe the potential for combining data analysis and knowledge-based systems in the context of production systems and describe two feasibility studies that demonstrate how knowledge-based systems can be created using data analysis. This article is part of the theme issue \'Towards symbiotic autonomous systems\'.

Multiple sclerosis (MS) is a neurological disorder that strikes the central nervous system. Due to the complexity of this disease, healthcare sectors are increasingly in need of shared clinical decision-making tools to provide practitioners with insightful knowledge and information about MS. These tools ought to be comprehensible by both technical and non-technical healthcare audiences. To aid this cause, this literature review analyzes the state-of-the-art decision support systems (DSSs) in MS research with a special focus on model-driven decision-making processes. The review clusters common methodologies used to support the decision-making process in classifying, diagnosing, predicting, and treating MS. This work observes that the majority of the investigated DSSs rely on knowledge-based and machine learning (ML) approaches, so the utilization of ontology and ML in the MS domain is observed to extend the scope of this review. Finally, this review summarizes the state-of-the-art DSSs, discusses the methods that have commonalities, and addresses the future work of applying DSS technologies in the MS field.

The milling process is a complex phenomenon dependent on various technological and material parameters. The development of a fluidized bed jet milling model is of high practical significance, since milling is utilized in many industries, and its complexity is still not sufficiently recognized. Therefore, this research aims to optimize fluidized bed jet milling with the use of fuzzy logic (FL) based approach as one of the primary artificial intelligence (AI) methods. The developed fuzzy logic model (FLMill) of the investigated process allows it to be described as a non-iterative procedure, over a wide range of operating conditions. Working air pressure, rotational speed of the classifier rotor, and time of conducting the test are considered as inputs, while mass and mean Sauter diameter of the product are defined as outputs. Several triangular and constant linguistic terms are used in the developed FLMill model, which was validated against the experimental data. The optimum working air pressure and the test\'s conducting time are 500 kPa and 3000 s, respectively. The optimum rotational speed of the classifier is equal to 50 s-1, considering the mass of the grinding product, and 250 s-1 for the mean Sauter diameter of the product. Such operating parameters allow obtaining 243.3 g of grinding product with the mean Sauter diameter of 11 µm. The research proved that the use of fuzzy logic modeling as a computer-based technique of solving mechanical engineering problems allows effective optimization of the fluidized bed jet milling process.

Processes like the care of type 2 diabetes mellitus patients require support by information systems considering the heterogeneity of the actors from different domains involved, enabling harmonization and integration of their specific methodologies and knowledge representation approaches towards interdisciplinary cooperation. Currently, the development of systems starts from the simplified information world, ignoring the aforementioned heterogeneity and specificity of real-world processes. This paper aims to demonstrate the feasibility of developing an adaptive, interoperable and intelligent system that supports the major aspects of type 2 diabetes mellitus care based on the Generic Component Model as formal methodology for modelling universal systems. The result is a deployable solution based on a formal representation of the diabetes care system, its objectives, and the intended business process. The implemented system enables reasoning over the data, inferring medical diagnosis. The effectiveness of the inference was evaluated, obtaining an F-measure of 0.89. The methods presented in this paper helps to build high quality models based on computation-independent aspects, which enable the construction of knowledge-based adaptive, intelligent and interoperable eHealth systems.

Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature for the modelling and assessment of MWL. In this paper, data related to two sets of tasks performed by participants under different conditions is reported. This data was gathered from different sets of questionnaires answered by these participants. These questionnaires were aimed at assessing the features believed by domain experts to influence overall mental workload. In total, 872 records are reported, each representing the answers given by a user after performing a task. On the one hand, collected data might support machine learning researchers interested in using predictive analytics for the assessment of mental workload. On the other hand, data, if exploited by a set of rules/arguments (as in [3]), may serve as knowledge-bases for researchers in the field of knowledge-based systems and automated reasoning. Lastly, data might serve as a source of information for mental workload designers interested in investigating the features reported here for mental workload modelling. This article was co-submitted from a research journal \"An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems\" [3]. The reader is referred to it for the interpretation of the data.