Jos De Roo

Diagnosis Support System based on clinical guidelines

Several studies have described the prevalence and severity of diagnostic errors. Diagnostic errors can arise from cognitive, training, educational and other issues. Examples of cognitive issues include flawed reasoning, incomplete knowledge, faulty information gathering or interpretation, and inappropriate use of decision-making heuristics. We describe a new approach, case-based fuzzy cognitive maps, for medical diagnosis and evaluate it by comparison with Bayesian belief networks. We created a semantic web framework that supports the two reasoning methods. We used database of 174 anonymous patients from several European hospitals: 80 of the patients were female and 94 male with an average age 45±16 (average±stdev). Thirty of the 80 female patients were pregnant. For each patient, signs/symptoms/observables/age/sex were taken into account by the system. We used a statistical approach to compare the two methods.

Three-dimensional information exchange over the semantic web for the domain of architecture, engineering, and construction

Abstract Three-dimensional (3-D) geometry can be described in many ways, with both a varying syntax and a varying semantics. As a result, several very diverse schemas and file formats can be deployed to describe geometry, depending on the application domain in question. In a multidisciplinary domain such as the domain of architecture, engineering, and construction, this range of specialized schemas makes file format conversions inevitable. The approach adopted by current conversion tools, however, often results in a loss of information, most often due to a “mistranslation” between different syntaxes and/or semantics, leading to errors and limitations in the design conception stage and to inefficiency due to the required remodeling efforts. An approach based on semantic web technology may reduce the loss of information significantly, leading to an improved processing of 3-D information and hence to an improved design practice in the architecture, engineering, and construction domain. This paper documents our investigation of the nature of this 3-D information conversion problem and how it may be encompassed using semantic web technology. In an exploratory double test case, we show how the specific deployment of semantic rule languages and an appropriate inference engine are to be adopted to improve this 3-D information exchange. It shows how semantic web technology allows the coexistence of diverse descriptions of the same 3-D information, interlinked through explicit conversion rules. Although only a simple example is used to document the process, and a more in-depth investigation is needed, the initial results indicate the suggested approach to be a useful alternative approach to obtain an improved 3-D information exchange.

Semantic patient information aggregation and medicinal decision support

Although the health care sector has already been subjected to a major computerization effort, this effort is often limited to the implementation of standalone systems which do not communicate with each other. Interoperability problems limit health care applications from achieving their full potential. In this paper, we propose the use of Semantic Web technologies to solve interoperability problems between data providers. Through the development of unifying health care ontologies, data from multiple health care providers can be aggregated, which can then be used as input for a decision support system. This way, more data is taken into account than a single health care provider possesses in his local setting. The feasibility of our approach is demonstrated by the creation of an end-to-end proof of concept, focusing on Belgian health care providers and medicinal decision support.

Drawing Conclusions from Linked Data on the Web: The EYE Reasoner

Linked data represents each piece of data as a link between two things. It lets software reasoners arrive at conclusions in a human-like way. This column discusses how the EYE reasoner exploits linked data and how industry is employing EYE.

Semantic processing of EHR data for clinical research

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.

Case Based Fuzzy Cognitive Maps (CBFCM): New method for medical reasoning: Comparison study between CBFCM/FCM

Doctor usually uses his experience from the clinical practice to confirm a diagnosis and to prescribe an appropriate treatment for a specific patient. The computerized medical reasoning should not only focus on existing medical knowledge but also on physicians previous experiences and new knowledge. Such knowledge and experience are vague and define uncertain relationships between facts and diagnosis. Case Based Fuzzy Cognitive Maps (CBFCM) are proposed as an evolution of Fuzzy Cognitive Maps (FCM) that allow more complete representation of knowledge since case-based fuzzy rules are introduced to improve FCM decision support systems. Semantic web is used to implement both FCM approaches. A database of 71 patients with urinary tract infections was used to perform the proposed approach. A comparative study between FCM (92%) and CBFCM (99%) was conducted and the results derived by CBFCM approach showed CBFCM to be superior to FCM.

A performance benchmark over semantic rule checking approaches in construction industry

As more and more architectural design and construction data is represented using the Resource Description Framework (RDF) data model, it makes sense to take advantage of the logical basis of RDF and implement a semantic rule checking process as it is currently not available in the architectural design and construction industry. The argument for such a semantic rule checking process has been made a number of times by now. However, there are a number of strategies and approaches that can be followed regarding the realization of such a rule checking process, even when limiting to the use of semantic web technologies. In this article, we compare three reference rule checking approaches that have been reported earlier for semantic rule checking in the domain of architecture, engineering and construction (AEC). Each of these approaches has its advantages and disadvantages. A criterion that is tremendously important to allow adoption and uptake of such semantic rule checking approaches, is performance. Hence, this article provides an overview of our collaborative test results in order to obtain a performance benchmark for these approaches. In addition to the benchmark, a documentation of the actual rule checking approaches is discussed. Furthermore, we give an indication of the main features and decisions that impact performance for each of these three approaches, so that system developers in the construction industry can make an informed choice when deciding for one of the documented rule checking approaches.

Querying and reasoning over large scale building data sets: an outline of a performance benchmark

The architectural design and construction domains work on a daily basis with massive amounts of data. Properly managing, exchanging and exploiting these data is an ever ongoing challenge in this domain. This has resulted in large semantic RDF graphs that are to be combined with a significant number of other data sets (building product catalogues, regulation data, geometric point cloud data, simulation data, sensor data), thus making an already huge dataset even larger. Making these big data available at high performance rates and speeds and into the correct (intuitive) formats is therefore an incredibly high challenge in this domain. Yet, hardly any benchmark is available for this industry that (1) gives an overview of the kind of data typically handled in this domain; and (2) that lists the query and reasoning performance results in handling these data. In this article, we therefore present a set of available sample data that explicates the scale of the situation, and we additionally perform a query and reasoning performance benchmark. This results not only in an initial set of quantitative performance results, but also in recommendations in implementing a web-based system relying heavily on large semantic data. As such, we propose an initial benchmark through which new upcoming data management proposals in the architectural design and construction domains can be measured.

An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies.

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

Semantics of Notation3 Logic: A Solution for Implicit Quantification

Since the development of Notation3 Logic, several years have passed in which the theory has been refined and used in practice by different reasoning engines such as cwm, FuXi or EYE. Nevertheless, a clear model-theoretic definition of its semantics is still missing. This leaves room for individual interpretations and renders it difficult to make clear statements about its relation to other logics such as DL or FOL or even about such basic concepts as correctness. In this paper we address one of the main open challenges: the formalization of implicit quantification. We point out how the interpretation of implicit quantifiers differs in two of the above mentioned reasoning engines and how the specification, proposed in the W3C team submission, could be formalized. Our formalization is then put into context by integrating it into a model-theoretic definition of the whole language. We finish our contribution by arguing why universal quantification should be handled differently than currently prescribed.