Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

Identification of novel molecular signatures of IgA nephropathy through an integrative -omics analysis

IgA nephropathy (IgAN) is the most prevalent among primary glomerular diseases worldwide. Although our understanding of IgAN has advanced significantly, its underlying biology and potential drug targets are still unexplored. We investigated a combinatorial approach for the analysis of IgAN-relevant -omics data, aiming at identification of novel molecular signatures of the disease. Nine published urinary proteomics datasets were collected and the reported differentially expressed proteins in IgAN vs. healthy controls were integrated into known biological pathways. Proteins participating in these pathways were subjected to multi-step assessment, including investigation of IgAN transcriptomics datasets (Nephroseq database), their reported protein-protein interactions (STRING database), kidney tissue expression (Human Protein Atlas) and literature mining. Through this process, from an initial dataset of 232 proteins significantly associated with IgAN, 20 pathways were predicted, yielding 657 proteins for further analysis. Step-wise evaluation highlighted 20 proteins of possibly high relevance to IgAN and/or kidney disease. Experimental validation of 3 predicted relevant proteins, adenylyl cyclase-associated protein 1 (CAP1), SHC-transforming protein 1 (SHC1) and prolylcarboxypeptidase (PRCP) was performed by immunostaining of human kidney sections. Collectively, this study presents an integrative procedure for -omics data exploitation, giving rise to biologically relevant results

An Intelligent Risk Detection Framework Using Knowledge Discovery To Improve Decision Efficiency In Healthcare Contexts: The Case Of Paediatric Congenital Heart Disease

Healthcare professionals, especially surgeons must make complex decisions with far reaching consequences and associated risks. As has been shown in other industries, the ability to drill down into pertinent data to explore knowledge behind the data greatly facilitates superior, informed decisions to ensue. This proposal proffers an Intelligent Risk Detection (IRD) Model using data mining techniques followed by Knowledge Discovery in order to detect the dominant risk factors across a complex surgical decision making process and thereby to predict the surgery results and hence support superior decision making. To illustrate the benefits of this model, the case of the Congenital Heart Disease (CHD) is presented[1]

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Medical 3D printing: methods to standardize terminology and report trends.

BackgroundMedical 3D printing is expanding exponentially, with tremendous potential yet to be realized in nearly all facets of medicine. Unfortunately, multiple informal subdomain-specific isolated terminological 'silos' where disparate terminology is used for similar concepts are also arising as rapidly. It is imperative to formalize the foundational terminology at this early stage to facilitate future knowledge integration, collaborative research, and appropriate reimbursement. The purpose of this work is to develop objective, literature-based consensus-building methodology for the medical 3D printing domain to support expert consensus.ResultsWe first quantitatively survey the temporal, conceptual, and geographic diversity of all existing published applications within medical 3D printing literature and establish the existence of self-isolating research clusters. We then demonstrate an automated objective methodology to aid in establishing a terminological consensus for the field based on objective analysis of the existing literature. The resultant analysis provides a rich overview of the 3D printing literature, including publication statistics and trends globally, chronologically, technologically, and within each major medical discipline. The proposed methodology is used to objectively establish the dominance of the term "3D printing" to represent a collection of technologies that produce physical models in the medical setting. We demonstrate that specific domains do not use this term in line with objective consensus and call for its universal adoption.ConclusionOur methodology can be applied to the entirety of medical 3D printing literature to obtain a complete, validated, and objective set of recommended and synonymous definitions to aid expert bodies in building ontological consensus

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

Big data analytics for preventive medicine

© 2019, Springer-Verlag London Ltd., part of Springer Nature. Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

A decision support methodology to enhance the competitiveness of the Turkish automotive industry

This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2013 Elsevier B.V. All rights reserved.Three levels of competitiveness affect the success of business enterprises in a globally competitive environment: the competitiveness of the company, the competitiveness of the industry in which the company operates and the competitiveness of the country where the business is located. This study analyses the competitiveness of the automotive industry in association with the national competitiveness perspective using a methodology based on Bayesian Causal Networks. First, we structure the competitiveness problem of the automotive industry through a synthesis of expert knowledge in the light of the World Economic Forum’s competitiveness indicators. Second, we model the relationships among the variables identified in the problem structuring stage and analyse these relationships using a Bayesian Causal Network. Third, we develop policy suggestions under various scenarios to enhance the national competitive advantages of the automotive industry. We present an analysis of the Turkish automotive industry as a case study. It is possible to generalise the policy suggestions developed for the case of Turkish automotive industry to the automotive industries in other developing countries where country and industry competitiveness levels are similar to those of Turkey