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Decision-making in healthcare often relies on narrative guidelines; however, these instruments are poorly accessible for supporting clinical decisionmaking. This study explores the application of rule-based decision logic in algorithmic modeling, emphasizing its great potential in clinical decision support and research. Integrating rule-based algorithms with existing information systems and real-world data poses a serious challenge. Integrating decision algorithms with information standards increases their effectiveness across various applications. This study outlines a method for constructing clinical decision trees (CDTs), highlighting their transparency and interpretability, using information standards as a design principle. We use the digitization of the Dutch breast cancer guideline through CDTs as a case study to exemplify their versatility and practical significance. The process step 'primary treatment' has been successfully translated from the narrative guidelines format to the anticipated ted computational format. [ABSTRACT FROM AUTHOR]

Treatment patterns in systemic anticancer therapy (SACT) are extremely varied and complex. While professional society guidelines exist that suggest recommended treatment strategies, these guidelines are produced through an extremely laborious and sometimes opaque manual process, making it impossible for such guidelines to cover all relevant treatment scenarios. To complement these manually curated guidelines, we leveraged a database of 5818 clinical trials and 7012 supporting references from 1943-present to calculate a quantifiable "relevance score". In a pilot evaluation, this score was strongly associated with professional society guideline recommendations, while also providing relevance information on thousands of additional therapies. We show that this score also accurately illustrates trends in SACT adoption over time. We foresee that this score, which comprehensively evaluates the relevance of SACT overall and by cancer subtype, will have utility for clinical practitioners as well as researchers in real-world data. [ABSTRACT FROM AUTHOR]

Representing knowledge in a comprehensible and maintainable way and transparently providing inferences thereof are important issues, especially in the context of applications related to artificial intelligence in medicine. This becomes even more obvious if the knowledge is dynamically growing and changing and when machine learning techniques are being involved. In this paper, we present an approach for representing knowledge about cancer therapies collected over two decades at St.-Johannes-Hospital in Dortmund, Germany. The presented approach makes use of InteKRator, a toolbox that combines knowledge representation and machine learning techniques, including the possibility of explaining inferences. An extended use of InteKRator's reasoning system will be introduced for being able to provide the required inferences. The presented approach is general enough to be transferred to other data, as well as to other domains. The approach will be evaluated, e. g., regarding comprehensibility, accuracy and reasoning efficiency. [ABSTRACT FROM AUTHOR]

Health and social care ecosystems are currently a matter of foundational organizational, methodological and technological paradigm changes towards personalized, preventive, predictive, participative precision (5P) medicine. For designing and implementing such advanced ecosystems, an understanding and correct representation of structure, function and relations of their components is inevitable. To guarantee consistent and conformant processes and outcomes, the specifications and principles must be internationally standardized. Summarizing the first author’s Keynotes over the last 15 years of pHealth conferences, the paper discusses concepts, standards and principles of 5P medicine ecosystems including their design and implementation. Furthermore, a guidance to find and to deploy corresponding international standards in practical projects is provided. [ABSTRACT FROM AUTHOR]

Nonalcoholic fatty liver disease (NAFLD) is a clinical syndrome, and pathologically characterized by diffuse macro-vesicular fatty change in the hepatocytes. NAFLD includes simple nonalcoholic fatty liver disease, nonalcoholic steatohepatitis (NASH) and hepatic cirrhosis. NASH is a disease evolving under the influence of various stimuli still poorly understood, but where insulin resistance is prominently. In this paper, we present a new diagnosis model to predict NASH. [ABSTRACT FROM AUTHOR]

Anecdotally, 38.5% of clinical outcome descriptions in randomized controlled trial publications contain complex text. Existing terminologies are insufficient to standardize outcomes and their measures, temporal attributes, quantitative metrics, and other attributes. In this study, we analyzed the semantic patterns in the outcome text in a sample of COVID-19 trials and presented a datadriven method for modeling outcomes. We conclude that a data-driven knowledge representation can benefit natural language processing of outcome text from published clinical studies. [ABSTRACT FROM AUTHOR]

Representing temporal information is a recurrent problem for biomedical ontologies. We propose a foundational ontology that combines the so-called threedimensional and four-dimensional approaches in order to be able to track changes in an individual and to trace his or her medical history. This requires, on the one hand, associating with any representation of an individual the representation of his or her life course and, on the other hand, distinguishing the properties that characterize this individual from those that characterize his or her life course. [ABSTRACT FROM AUTHOR]

Our understanding of the impact of interventions in critical care is limited by the lack of techniques that represent and analyze complex intervention spaces applied across heterogeneous patient populations. Existing work has mainly focused on selecting a few interventions and representing them as binary variables, resulting in oversimplification of intervention representation. The goal of this study is to find effective representations of sequential interventions to support intervention effect analysis. To this end, we have developed Hi-RISE (Hierarchical Representation of Intervention Sequences), an approach that transforms and clusters sequential interventions into a latent space, with the resulting clusters used for heterogeneous treatment effect analysis. We apply this approach to the MIMIC III dataset and identified intervention clusters and corresponding subpopulations with peculiar odds of 28-day mortality. Our approach may lead to a better understanding of the subgrouplevel effects of sequential interventions and improve targeted intervention planning in critical care settings. [ABSTRACT FROM AUTHOR]

Complex interventions are ubiquitous in healthcare. A lack of computational representations and information extraction solutions for complex interventions hinders accurate and efficient evidence synthesis. In this study, we manually annotated and analyzed 3,447 intervention snippets from 261 randomized clinical trial (RCT) abstracts and developed a compositional representation for complex interventions, which captures the spatial, temporal and Boolean relations between intervention components, along with an intervention normalization pipeline that automates three tasks: (i) treatment entity extraction; (ii) intervention component relation extraction; and (iii) attribute extraction and association. 361 intervention snippets from 29 unseen abstracts were included to report on the performance of the evaluation. The average Fmeasure was 0.74 for treatment entity extraction on an exact match and 0.82 for attribute extraction. The F-measure for relation extraction of multi-component complex interventions was 0.90. 93% of extracted attributes were correctly attributed to corresponding treatment entities. [ABSTRACT FROM AUTHOR]

The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable semantics in frequently used Common Data Models, there appears to be a gap between knowledge representation requirements and these models. In this use-case oriented approach, we explore how a system-theoretic, architecture-centric, ontology-based methodology can help to better understand this gap. We show how using the Generic Component Model helps to analyze the data management system in a way that allows accounting for data management procedures inside the system and knowledge representation of the real world at the same time. [ABSTRACT FROM AUTHOR]

For meeting the challenge of aging, multi-diseased societies, cost containment, workforce development and consumerism by improved care quality and patient safety as well as more effective and efficient care processes, health and social care systems around the globe undergo an organizational, methodological and technological transformation towards personalized, preventive, predictive, participative precision medicine (P5 medicine). This paper addresses chances, challenges and risks of specific disruptive methodologies and technologies for the transformation of health and social care systems, especially focusing on the deployment of intelligent and autonomous systems. [ABSTRACT FROM AUTHOR]

Expert systems have a long tradition in both medical informatics and artificial intelligence research. Traditionally, such systems are created by implementing knowledge provided by experts in a system that can be queried for answers. To automatically generate such knowledge directly from data, the lightweight InteKRator toolbox will be introduced here, which combines knowledge representation and machine learning approaches. The learned knowledge is represented in the form of rules with exceptions that can be inspected and that are easily comprehensible. An inference module allows for the efficient answering of queries, while at the same time offering the possibility of providing explanations for the inference results. The learned knowledge can be revised manually or automatically with new evidence after learning. [ABSTRACT FROM AUTHOR]

The paper discusses requirements and solutions for design and management of transformed health ecosystems. After introducing related definitions with reference to the transformation of health to P5 medicine, basics on systems, knowledge representation and management as well as system development processes and their formal representation/modelling from the perspectives of systems theory, theory of knowledge, languages and grammars are considered in some detail. As result, the ISO 23903 reference architecture is shortly introduced and compared with other existing approaches and standards. [ABSTRACT FROM AUTHOR]

Interoperability issues are common in biomedical informatics. Reusing data generated from a system in another system, or integrating an existing clinical decision support system (CDSS) in a new organization is a complex task due to recurrent problems of concept mapping and alignment. The GL-DSS of the DESIREE project is a guideline-based CDSS to support the management of breast cancer patients. The knowledge base is formalized as an ontology and decision rules. OncoDoc is another CDSS applied to breast cancer management. The knowledge base is structured as a decision tree. OncoDoc has been routinely used by the multidisciplinary tumor board physicians of the Tenon Hospital (Paris, France) for three years leading to the resolution of 1,861 exploitable decisions. Because we were lacking patient data to assess the DESIREE GL-DSS, we investigated the option of reusing OncoDoc patient data. Taking into account that we have two CDSSs with two formalisms to represent clinical practice guidelines and two knowledge representation models, we had to face semantic and structural interoperability issues. This paper reports how we created 10,681 synthetic patients to solve these issues and make OncoDoc data re-usable by the GL-DSS of DESIREE. [ABSTRACT FROM AUTHOR]

Multidisciplinary and highly dynamic pHealth ecosystems according to the 5P Medicine paradigm require careful consideration of systems integration and interoperability within the domains knowledge space. The paper addresses the different aspects or levels of knowledge representation (KR) and management (KM) from cognitive theories (theories of knowledge) and modeling processes through notation up to processing, tooling and implementation. Thereby, it discusses language and grammar challenges and constraints, but also development process aspects and solutions, so demonstrating the limitation of data level considerations. Finally, it presents the ISO 23903 Interoperability and Integration Reference Architecture to solve the addressed problems and to correctly deploy existing standards and work products at any representational level including data models as well as data model integration and interoperability. [ABSTRACT FROM AUTHOR]

This study proposes an ontology model to represent the surgical pathways for appendicectomy, incorporating domain knowledge extracted from literature and electronic records in an Australian health district. The ontology comprised 108 concepts and 81 object properties. The model is useful for continuous quality improvement initiatives, i.e., process mining to understand what happens in the hospital versus what is required by the policy. [ABSTRACT FROM AUTHOR]

Context: Arden Syntax is a standard that encodes knowledge as Medical Logic Modules but lacks a standard query data model and terminology. Objective: Assess FHIR-enabled data access in a proposed version of Arden Syntax. Method: Queries in 340 MLMs were tabulated. Result: FHIR can serve as the Arden Syntax data model, but nonstandard terminologies still impede interoperability. Conclusion: The proposed Arden Syntax v3.0 FHIR-enabled data access is adequate as a query data model for MLMs. [ABSTRACT FROM AUTHOR]

The number of features that can be extracted from ECG signals has increased with the advancement in signal processing techniques. At the same time, there is an increase in research efforts to support efficient and effective analysis and interpretation of these signals. In this paper, we propose the use of ontology for knowledge representation and discovery of ECG data. Given the lack of a widely acceptable standards, the use of ontology can support the establishment of common understanding of the kind of knowledge that can be extracted from the ECG data and shared among various heterogeneous systems. The proposed ontology is both platform and application independent. Furthermore, it is possible to enrich the proposed ontology with new knowledge that may not explicitly be expressed in the data. [ABSTRACT FROM AUTHOR]

This paper presents medical knowledge representation of data provided within Clinical Practice Guidelines for Heart Failure. The formalization is provided in order to support taking decisions on an appropriate treatment strategy for a specific patient. An intuitive and efficient mechanism of medical knowledge formalization, called extended Timed Transition Diagram (eTTD), is used to represent acquired medical knowledge. The presented models can be used to help students in their training as well as to support physicians with their decision-making tasks. [ABSTRACT FROM AUTHOR]

Optical navigation systems help surgeons find their way through the complex anatomy of a patient. However, such systems are accident-sensitive, timeconsuming and difficult to use because of their complicated technical requirements such as the setting of optical markers and their intraoperative registration. The BIOPASS project, therefore, provides an innovative localisation system for markerless navigation in endoscopic surgery to support medical decision making. This system comprises several machine learning classifiers to recognise anatomical structures visible in the endoscopic images. To verify the data provided by these classifiers and to alert medical staff about surgical risk situations, we developed a new ontology-based software called OntoSun. Our software improves the precision and the sustainable traceability of the classifiers' results and also provides warning messages that increase situational awareness during surgical interventions. [ABSTRACT FROM AUTHOR]

The formalization of expert knowledge enables a broad spectrum of applications employing ontologies as underlying technology. These include eLearning, Semantic Web and expert systems. However, the manual construction of such ontologies is time-consuming and thus expensive. Moreover, experts are often unfamiliar with the syntax and semantics of formal ontology languages such as OWL and usually have no experience in developing formal ontologies. To overcome these barriers, we developed a new method and tool, called Expert2OWL that provides efficient features to support the construction of OWL ontologies using GFO (General Formal Ontology) as a top-level ontology. This method allows a close and effective collaboration between ontologists and domain experts. Essentially, this tool integrates Excel spreadsheets as part of a patternbased ontology development and refinement process. Expert2OWL enables us to expedite the development process and modularize the resulting ontologies. We applied this method in the field of Chinese Herbal Medicine (CHM) and used Expert2OWL to automatically generate an accurate Chinese Herbology ontology (CHO). The expressivity of CHO was tested and evaluated using ontology query languages SPARQL and DL. CHO shows promising results and can generate answers to important scientific questions such as which Chinese herbal formulas contain which substances, which substances treat which diseases, and which ones are the most frequently used in CHM. [ABSTRACT FROM AUTHOR]

Published as textual documents, clinical practice guidelines (CPGs) didn't demonstrate to impact physician practices when disseminated in their original format. However, when computerized and embedded in clinical decision support systems, they appeared to be more effective. In order to ease the translation from textual to computerized CPGs, we have elaborated a fine-grained knowledge model of CPGs (FGKM) to be used when authoring CPGs. The work has been conducted on VIDALRecos® CPGs. The building of the model has followed a bottom-up iterative process starting with 15 different CPGs. The first version of the FGKM has been assessed on two new complex CPGs, and was enriched by comparison with the Guideline Elements Model (GEM). The final version of the FGKM has been tested on the 2014 Hypertension CPGs. We compared the rules automatically derived from FGKM instances to those manually extracted from textual CPGs for decision support. Results showed that difficulties such as text normalization have to be solved. The FGKM is intended to be used upstream of the process of CPGs authoring in order to ease the implementation and the update of both textual and computerized CPGs. [ABSTRACT FROM AUTHOR]

The Arden Syntax for Medical Logic Modules is a standardized and wellestablished programming language to represent medical knowledge. To test the compliance level of existing compilers and interpreters no public test suite exists. This paper presents the research to transform the specification into a set of unit tests, represented in JUnit. It further reports on the utilization of the test suite testing four different Arden Syntax processors. The presented and compared results reveal the status conformance of the tested processors. How test driven development of Arden Syntax processors can help increasing the compliance with the standard is described with two examples. In the end some considerations how an open source test suite can improve the development and distribution of the Arden Syntax are presented. [ABSTRACT FROM AUTHOR]

The realization of ontology visualization requirements in university education is a challenging task and should be supported by appropriate tools. This applies in particular, if the ontology to be visualized is based on a large text corpus that comprises a huge number of concepts, relations and annotations. In SNIK, we developed such an ontology of information management in hospitals in order to support the transfer of knowledge in the context of the university education. The challenge is to identify tools and methods, which are capable to support ontology visualization and usage as efficiently as possible. Related research fields (e.g. bioinformatics) are confronted with similar visualization problems. These tools and methods used could provide a suitable solution in our research field. In total, we assessed eight tools concerning the visualization of large ontologies to evaluate their suitability representing knowledge in the field of medical informatics. [ABSTRACT FROM AUTHOR]

Background: CIGs languages consist of approach specific concepts. More widely used concepts, such as those in UMLS are not typically used. Objective: An evaluation of UMLS concept sufficiency for CIG definition. Method: A popular guideline is mapped to UMLS concepts with NLP. Results are reviewed to evaluate gaps, and appropriateness. Results: A significant number of the guideline text mapped to UMLS concepts. Conclusions: The approach has shown promise and highlighted further challenges. [ABSTRACT FROM AUTHOR]

Context: Arden Syntax encodes knowledge as Medical Logic Modules (MLMs). Current work involves improvement of its representation of business processes. Objective: Assess how DMN can represent decision-making in MLMs. Method: A sample of 340 MLMs was tabulated for DMN representation. Result: While DMN can represent all MLMs at the decision requirements level, some are not supported at the decision logic level. Conclusion: DMN facilitates representation of business processes but would not address all decision logic. [ABSTRACT FROM AUTHOR]

Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but holds also in other clinical domains. We have recently developed a prototype system, CliniText, which, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge, combines the computational mechanisms of knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data. We demonstrate our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating summaries in two very different domains - Diabetes Management and Cardiothoracic surgery. In particular, we explain the process of generating a discharge summary of a patient who had undergone a Coronary Artery Bypass Graft operation, and a brief summary of the treatment of a diabetes patient for five years. [ABSTRACT FROM AUTHOR]

This paper introduces a knowledge representation for smart home rules and scenarios. We have created a rule base based on the rules found during a literature review. Following this, a general rule concept was derived from the identified categories. Furthermore, subcategories for certain contexts in the smart home environment were identified and illustrated in the results by means of an extract of the rules found in the literature. Based on this information, a hierarchically structured nomenclature with four axes has been developed and evaluated. [ABSTRACT FROM AUTHOR]

The DICOM standard is ubiquitous within medicine. However, improved DICOM semantics would significantly enhance search operations. Furthermore, databases of current PACS systems are not flexible enough for the demands within image analysis research. In this paper, we investigated if we can use Semantic Web technology, to store and represent metadata of DICOM image files, as well as linking additional computational results to image metadata. Therefore, we developed a proof of concept containing two applications: one to store commonly used DICOM metadata in an RDF repository, and one to calculate imaging biomarkers based on DICOM images, and store the biomarker values in an RDF repository. This enabled us to search for all patients with a gross tumor volume calculated to be larger than 50 cc. We have shown that we can successfully store the DICOM metadata in an RDF repository and are refining our proof of concept with regards to volume naming, value representation, and the applications themselves. [ABSTRACT FROM AUTHOR]

The Radiology Gamuts Ontology (RGO) is a knowledge model of diseases, interventions, and imaging manifestations. RGO incorporates 16,822 terms with their synonyms and abbreviations and 55,393 relationships between terms. Subsumption defines the relationship between more general and more specific terms; causality relates disorders and their imaging manifestations. We explored the application of the RGO to build an interactive decision support system for radiological diagnosis. The Gamuts DDx system was created to apply the RGO's knowledge: it identifies a list of potential diagnoses in response to one or more user-specified imaging observations. The system also identifies a set of observations that allow one to narrow the diagnosis, and dynamically narrows or expands the list of diagnoses as imaging findings are selected or deselected. The functionality has been implemented as a web-based user interface and as a web service. The current work demonstrates the feasibility of exploiting the RGO's causal knowledge to provide interactive decision support for diagnosis of imaging findings. Ongoing efforts include the further development of the system's knowledge base and evaluation of the system in clinical use. [ABSTRACT FROM AUTHOR]

The successful adoption by clinicians of evidence-based clinical practice guidelines (CPGs) contained in clinical information systems requires efficient translation of free-text guidelines into computable formats. Natural language processing (NLP) has the potential to improve the efficiency of such translation. However, it is laborious to develop NLP to structure free-text CPGs using existing formal knowledge representations (KR). In response to this challenge, this vision paper discusses the value and feasibility of supporting symbiosis in text-based knowledge acquisition (KA) and KR. We compare two ontologies: (1) an ontology manually created by domain experts for CPG eligibility criteria and (2) an upper-level ontology derived from a semantic pattern-based approach for automatic KA from CPG eligibility criteria text. Then we discuss the strengths and limitations of interweaving KA and NLP for KR purposes and important considerations for achieving the symbiosis of KR and NLP for structuring CPGs to achieve evidence-based clinical practice. [ABSTRACT FROM AUTHOR]

To provide best recommendations at the point of care, guidelines have been implemented in computer systems. As a prerequisite, guidelines are translated into a computer-interpretable guideline format. Since there are no specific tools to translate nursing guidelines, only a few nursing guidelines are translated and implemented in computer systems. Unified modeling language (UML) is a software writing language and is known to well and accurately represent end-users’ perspective, due to the expressive characteristics of the UML. In order to facilitate the development of computer systems for nurses’ use, the UML was used to translate a paper-based nursing guideline, and its ease of use and the usefulness were tested through a case study of a genetic counseling guideline. The UML was found to be a useful tool to nurse informaticians and a sufficient tool to model a guideline in a computer program. [ABSTRACT FROM AUTHOR]

At present, there are no widely accepted, standard approaches for representing computer-based clinical decision support (CDS) intervention types and their structural components. This study aimed to identify key requirements for the representation of five widely utilized CDS intervention types: alerts and reminders, order sets, infobuttons, documentation templates/forms, and relevant data presentation. An XML schema was proposed for representing these interventions and their core structural elements (e.g., general metadata, applicable clinical scenarios, CDS inputs, CDS outputs, and CDS logic) in a shareable manner. The schema was validated by building CDS artifacts for 22 different interventions, targeted toward guidelines and clinical conditions called for in the 2011 Meaningful Use criteria. Custom style sheets were developed to render the XML files in human-readable form. The CDS knowledge artifacts were shared via a public web portal. Our experience also identifies gaps in existing standards and informs future development of standards for CDS knowledge representation and sharing. [ABSTRACT FROM AUTHOR]

Prediction of synergistic or antagonistic effects of drug-drug interaction (DDI) in vivo has been of considerable interest over the years. Formal representation of pharmacological knowledge such as ontology is indispensable for machine reasoning of possible DDIs. However, current pharmacology knowledge bases are not sufficient to provide formal representation of DDI information. With this background, this paper presents: (1) a description framework of pharmacodynamics ontology; and (2) a methodology to utilize pharmacodynamics ontology to detect different types of possible DDI pairs with supporting information such as underlying pharmacodynamics mechanisms. We also evaluated our methodology in the field of drugs related to noradrenaline signal transduction process and 11 different types of possible DDI pairs were detected. The main features of our methodology are the explanation capability of the reason for possible DDIs and the distinguishability of different types of DDIs. These features will not only be useful for providing supporting information to prescribers, but also for large-scale monitoring of drug safety. [ABSTRACT FROM AUTHOR]

An important case for successful deployment of a lifetime electronic health record is reuse of clinical data from the electronic health record (EHR) for epidemiology, reimbursement, and research. We report a collaboration between the IHTSDO and the WHO to develop knowledge-based tools supporting translation of data from SNOMED CT to the ICD-10 classification. These tools have been vetted by an international community and are available for system vendors to enhance the interoperability of their products. The maps we created are also informing the development of the next generation of classifications which will employ a common ontology base between SNOMED CT and ICD-11 to promote interoperability. [ABSTRACT FROM AUTHOR]

We translated an existing English negation lexicon (NegEx) to Swedish, French, and German and compared the lexicon on corpora from each language. We observed Zipf's law for all languages, i.e., a few phrases occur a large number of times, and a large number of phrases occur fewer times. Negation triggers 'no' and 'not' were common for all languages; however, other triggers varied considerably. The lexicon is available in OWL and RDF format and can be extended to other languages. We discuss the challenges in translating negation triggers to other languages and issues in representing multilingual lexical knowledge. [ABSTRACT FROM AUTHOR]

Assessing the quality of and integrating clinical trial reports are necessary to practice evidence-based medicine. In particular, the numerical data is essential to understanding the strength and quality of the clinical trial study. In this paper, we present a formal representation for standardizing numerical data in published clinical trial reports, and our efforts towards developing computational tools to capture and visualize this representation. The approach includes two aspects: a process model used to precisely define experimental context behind the numerical value; and a spreadsheet, an intuitive and familiar tool used to organize numerical data. We demonstrated this representation using clinical trial reports on non-small cell lung cancer (NSCLC). We performed a preliminary evaluation to determine the usefulness of this formalism for identifying the characteristics, quality and significance of a clinical trial. Our initial results demonstrate that the representation is sufficiently expressive to capture reported numerical information in published papers. [ABSTRACT FROM AUTHOR]

Traditionally, rule interactions are handled at implementation time through rule task properties that control the order in which rules are executed. By doing so, knowledge about the behavior and interactions of decision rules is not captured at modeling time. We argue that this is important knowledge that should be integrated in the modeling phase. In this project, we build upon current work on a conceptual schema to represent clinical knowledge for decision support in the form of if then rules. This schema currently captures provenance of the clinical content, context where such content is actionable (i.e. constraints) and the logic of the rule itself. For this project, we borrowed concepts from both the Semantic Web (i.e., Ontologies) and Complex Adaptive Systems (CAS), to explore a conceptual approach for modeling rule interactions in an enterprise-wide clinical setting. We expect that a more comprehensive modeling will facilitate knowledge authoring, editing and update; foster consistency in rules implementation and maintenance; and develop authoritative knowledge repositories to promote quality, safety and efficacy of healthcare. [ABSTRACT FROM AUTHOR]

Based on the paradigm changes for health, health services and underlying technologies as well as the need for at best comprehensive and increasingly automated interoperability, the paper addresses the challenge of knowledge representation and management for medical decision support. After introducing related definitions, a system-theoretical, architecture-centric approach to decision support systems (DSSs) and appropriate ways for representing them using systems of ontologies is given. Finally, existing and emerging knowledge representation and management standards are presented. The paper focuses on the knowledge representation and management part of DSSs, excluding the reasoning part from consideration. [ABSTRACT FROM AUTHOR]

In order to be able to automatically calculate clinical quality indicators, we have proposed CLIF, a stepwise method for clinical quality indicator formalisation. Quality indicators are used for external accountability and hospital comparison. As clinical quality indicators are computed in a decentralised manner by the hospitals themselves, reproducibility of the formalisation method is essential to ensure the comparability of calculated values. Thus, we performed a case study to investigate the reproducibility of CLIF. Eight participants formalised the same sample quality indicator with the help of a web-based indicator-authoring tool that facilitates the application of CLIF. We analysed the results per step and concluded that the method itself leads to reproducible results. To further improve reproducibility, ambiguities in the indicator text must be clarified and trained experts are needed to encode clinical concepts and to specify the relations between concepts. [ABSTRACT FROM AUTHOR]

This work addresses the problem of medical terminologies building. Starting from a set of elementary knowledge, a multi-hierarchical terminology is generated according to formal principles that are based on explicit classificatory points of view. The experimentation has been carried out on kidney diseases. Hierarchical conceptual structures are generated starting from Conceptual Graph based definition of kidney disease, a hierarchy of defining types and a set of organizing rules. The generated hierarchy includes new potential disease concepts with different level of likeliness. This 'ready to use' generated multi-hierarchical terminology can be used for coding, includes new potential terms for the future and, due to the formalization of classificatory principles, offers improved opportunities of diseases instances aggregation. These features are looking very promising in the context of epidemiological observation tools (registries, long term cohort studies). They offer mean to meet two critical requirements: the statistical continuity in data analysis and the sustainable management of the underlying terminological system. [ABSTRACT FROM AUTHOR]

Incorrect or improper diagnostic tests uses have important implications for health outcomes and costs. Clinical Decision Support Systems purports to optimize the use of diagnostic tests in clinical practice. The computerized medical reasoning should not only focus on existing medical knowledge but also on physician's previous experiences and new knowledge. Such medical knowledge is vague and defines uncertain relationships between facts and diagnosis, in this paper, Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps. They allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve diagnosis decision. We have developed a framework for interacting with patient's data and formalizing knowledge from Guidelines in the domain of Urinary Tract Infection. The conducted study allowed us to test cognitive approaches for implementing Guidelines with Semantic Web tools. The advantage of this approach is to enable the sharing and reuse of knowledge from Guidelines, physicians experiences and simplify maintenance. [ABSTRACT FROM AUTHOR]

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data. [ABSTRACT FROM AUTHOR]

Semantic interoperability is one of the priority themes of the ARGOS Trans-Atlantic Observatory. This topic represents a globally recognised challenge that must be addressed if electronic health records are to be shared among heterogeneous systems, and the information in them exploited to the maximum benefit of patients, professionals, health services, research, and industry. Progress in this multi-faceted challenge has been piecemeal, and valuable lessons have been learned, and approaches discovered, in Europe and in the US that can be shared and combined. Experts from both continents have met at three ARGOS workshops during 2010 and 2011 to share understanding of these issues and how they might be tackled collectively from both sides of the Atlantic. This policy brief summarises the problems and the reasons why they are important to tackle, and also why they are so difficult. It outlines the major areas of semantic innovation that exist and that are available to help address this challenge. It proposes a series of next steps that need to be championed on both sides of the Atlantic if further progress is to be made in sharing and analysing electronic health records meaningfully. Semantic interoperability requires the use of standards, not only for EHR data to be transferred and structurally mapped into a receiving repository, but also for the clinical content of the EHR to be interpreted in conformity with the original meanings intended by its authors. Wide-scale engagement with professional bodies, globally, is needed to develop these clinical information standards. Accurate and complete clinical documentation, faithful to the patient's situation, and interoperability between systems, require widespread and dependable access to published and maintained collections of coherent and quality-assured semantic resources, including models such as archetypes and templates that would (1) provide clinical context, (2) be mapped to interoperability standards for EHR data, (3) be linked to well specified multi-lingual terminology value sets, and (4) be derived from high quality ontologies. There is need to gain greater experience in how semantic resources should be defined, validated, and disseminated, how users (who increasingly will include patients) should be educated to improve the quality and consistency of EHR documentation and to make full use of it. There are urgent needs to scale up the authorship, acceptance, and adoption of clinical information standards, to leverage and harmonise the islands of standardisation optimally, to assure the quality of the artefacts produced, and to organise end-to-end governance of the development and adoption of solutions. [ABSTRACT FROM AUTHOR]

The new generation of health information standards, where the syntax and semantics of the content is explicitly formalized, allows for interoperability in healthcare scenarios and analysis in clinical research settings. Studies involving clinical and genomic data include accumulating knowledge as relationships between genotypic and phenotypic information as well as associations within the genomic and clinical worlds. Some involve analysis results targeted at a specific disease; others are of a predictive nature specific to a patient and may be used by decision support applications. Representing knowledge is as important as representing data since data is more useful when coupled with relevant knowledge. Any further analysis and cross-research collaboration would benefit from persisting knowledge and data in a unified way. This paper describes a methodology used in Hypergenes, an EC FP7 project targeting Essential Hypertension, which captures data and knowledge using standards such as HL7 CDA and Clinical Genomics, aligned with the CEN EHR 13606 specification. We demonstrate the benefits of such an approach for clinical research as well as in healthcare oriented scenarios. [ABSTRACT FROM AUTHOR]

The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,00o deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CTTM codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of 'Drug' HasIndication 'SNOMED CTTM code'. The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care. [ABSTRACT FROM AUTHOR]

Taking account of the great availability of Petri nets in modeling and analyzing large complicated signaling networks, semantics of Petri nets is in need of systematization for the purpose of consistency and reusability of the models. This paper reports on standardization of units of Petri nets on the basis of an ontology that gives an intrinsic definition to the process of signaling in signaling pathways. [ABSTRACT FROM AUTHOR]

Although game theory has been first invented to reason with economic scenarios with rational agents, it has since been extended into many other fields including biological and medical sciences. In this paper we propose to model the interactions between virus and human in an influenza epidemic in a two player, adversarial game scenario with multiple levels of abstraction. As conventional game representations are inadequate in this complex problem domain, we propose Object Oriented Multi-Agent Influence Diagrams (OO-MAID), a novel graphical representation for multi-level games, which takes advantage of both organizational information and probabilistic independence in the problem domain. The OO-MAID representation can be readily applied in similar medical independent characteristics. We demonstrate the feasibility of this novel approach with sample models in the domain. [ABSTRACT FROM AUTHOR]

Clinical Communication failures are considered the leading cause of medical errors [1]. Minimizing communication problems among clinical team members could directly reduce medical errors and hence, increase patient safety and improve health care quality. Our main focus is, through knowledge representation approach, to develop an understanding of communication problems applied to health care settings. This will serve as the foundation to our long term goal of building an ontology-driven educational tool that will be used to educate clinicians about miscommunication issues and as a means to improve it. [ABSTRACT FROM AUTHOR]

The aims of this study were to develop data specifications for nursing problems related to maternal nursing care and to test the applicability of those data specifications. First, we identified focus concepts and characterizing concepts of nursing problems by analyzing nursing-problem statements from nursing records, reviewing the literature, and interviewing experts. Second, we identified relationships between the focus concepts and characterizing concepts. Third, value sets of characterizing concepts were identified and types and cardinalities of the characterizing concepts were defined based on those value sets. Finally, data specifications were evaluated by a group of experts and by applying them to published case reports. The adequacy of the characterizing concepts and value sets, and the types and cardinalities of the characterizing concepts were validated. In total, 58 data specifications were developed with 53 characterizing concepts, relationships, and value sets. Their validity was established by the experts and by their application to case reports. The data specifications developed in this study can ensure that electronic health records contain meaningful and valid information, and support the semantic interoperability of nursing information. [ABSTRACT FROM AUTHOR]