Eider Sanchez

Decisional DNA: A multi-technology shareable knowledge structure for decisional experience

Knowledge representation and engineering techniques are becoming useful and popular components of hybrid integrated systems used to solve complicated practical problems in different disciplines. These techniques offer features such as: learning from experience, handling noisy and incomplete data, helping with decision making, and predicting capabilities. In this paper, we present a multi-domain knowledge representation structure called Decisional DNA that can be implemented and shared for the exploitation of embedded knowledge in multiple technologies. Decisional DNA, as a knowledge representation structure, offers great possibilities on gathering explicit knowledge of formal decision events as well as a tool for decision making processes. Its applicability is shown in this paper when applied to different decisional technologies. The main advantages of using the Decisional DNA rely on: (i) versatility and dynamicity of the knowledge structure, (ii) storage of day-to-day explicit experience in a single structure, (iii) transportability and shareability of the knowledge, and (iv) predicting capabilities based on the collected experience. Thus, after analysis and results, we conclude that the Decisional DNA, as a unique multi-domain structure, can be applied and shared among multiple technologies while enhancing them with predicting capabilities and facilitating knowledge engineering processes inside decision making systems.

USING SET OF EXPERIENCE KNOWLEDGE STRUCTURE TO EXTEND A RULE SET OF CLINICAL DECISION SUPPORT SYSTEM FOR ALZHEIMER'S DISEASE DIAGNOSIS

In this article we present an experience-based clinical decision support system (CDSS) for the diagnosis of Alzheimers disease, which enables the discovery of new knowledge in the system and the generation of new rules that drive reasoning. In order to evolve an initial set of production rules given by medical experts we make use of the Set of Experience Knowledge Structure (SOEKS). An illustrative case of our system is also presented.

Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer

The integration of Clinical Decision Support Systems (CDSS) in nowadays clinical environments has not been fully achieved yet. Although numerous approaches and technologies have been proposed since 1960, there are still open gaps that need to be bridged. In this work we present advances from the established state of the art, overcoming some of the most notorious reported difficulties in: (i) automating CDSS, (ii) clinical workflow integration, (iii) maintainability and extensibility of the system, (iv) timely advice, (v) evaluation of the costs and effects of clinical decision support, and (vi) the need of architectures that allow the sharing and reusing of CDSS modules and services. In order to do so, we introduce a new clinical task model oriented to clinical workflow integration, which follows a federated approach. Our work makes use of the reported benefits of semantics in order to fully take advantage of the knowledge present in every stage of clinical tasks and the experience acquired by physicians. In order to introduce a feasible extension of classical CDSS, we present a generic architecture that permits a semantic enhancement, namely Semantic CDSS (S-CDSS). A case study of the proposed architecture in the domain of breast cancer is also presented, pointing some highlights of our methodology.

A Knowledge-based Clinical Decision Support System for the diagnosis of Alzheimer Disease

Alzheimer Disease (AD) has become a major issue in developed countries due to medical advances that have extended the population longevity. Recent advances in early detection date the initial stages of AD several years before the first recognizable symptoms appear visible.

Decisional DNA for modeling and reuse of experiential clinical assessments in breast cancer diagnosis and treatment

Clinical Decision Support Systems (CDSS) are active knowledge resources that use patient data to generate case specific advice. The fast pace of change of clinical knowledge imposes to CDSS the continuous update of the domain knowledge and decision criteria. Traditional approaches require costly tedious manual maintenance of the CDSS knowledge bases and repositories. Often, such an effort cannot be assumed by medical teams, hence maintenance is often faulty. In this paper, we propose a (semi-)automatic update process of the underlying knowledge bases and decision criteria of CDSS, following a learning paradigm based on previous experiences, such as the continuous learning that clinicians carry out in real life. In this process clinical decisional events are acquired and formalized inside the system by the use of the SOEKS and Decisional DNA experiential knowledge representation techniques. We propose three algorithms processing clinical experience to: (a) provide a weighting of the different decision criteria, (b) obtain their fine-tuning, and (c) achieve the formalization of new decision criteria. Finally, we present an implementation instance of a CDSS for the domain of breast cancer diagnosis and treatment.

An architecture for the semantic enhancement of clinical decision support systems

Clinical Decision Support Systems (CDSS) are useful tools that aid physicians during different tasks such as diagnosis, treatment and patient monitoring. Multidisciplinary, heterogeneous and disperse clinical information and decision criteria have to be handled by CDSSs. For such tasks, Knowledge Engineering (KE) techniques and semantic technologies are very suitable, as they support (i) the integration of heterogeneous knowledge, (ii) the expression of rich and well-defined models for knowledge aggregation, and (iii) the application of logic reasoning for the generation of new knowledge. In this paper we propose a generic architecture of a CDSS based on semantic technologies, which also considers the reutilization and enhancement of former CDSS in an organization. Particularly, an implementation of the proposed architecture is also presented, aiming to support the early diagnosis of AD.

IMPACT OF REFLEXIVE ONTOLOGIES IN SEMANTIC CLINICAL DECISION SUPPORT SYSTEMS

Ontology processing is arguably a time-consuming process with high associated computational costs. Query actions constitute a crucial part of the reasoning process and are a primary source of time consumption. Reflexive ontologies (ROs) is a novel approach intended to reduce time consumption problems while providing a fast reaction from ontology-based applications. In this article we present the implementation of a knowledge-based clinical decision support system (CDSS) for the diagnosis of Alzheimers disease, which was the benchmark used to evaluate the impact of RO in the overall performance of the system. The implementation details and the definition of the implementation methodology are exposed in this article, along with the results of the evaluation. Some novel techniques that aim to optimize the performance of ROs are also presented with highlights of the test application introduced in our previous work.

Augmenting Guideline-based CDSS with Experts’ Knowledge

Over the past years, clinical guidelines have increasingly become part of the clinical daily practice in order to provide best available Evidence-Based-Medicine services. Hence, their formalization as computer interpretable guidelines (CIG) and their implementation in clinical decision support systems (CDSSs) are emerging to support clinicians in their decision making process and potentially improve medical outcomes. However, guideline compliancy in the clinical daily practice is still “low”. Some of the reasons for such low compliance rate are (i) lack of a complete guideline to cover special clinical cases (e.g. oncogeriatric cases), (ii) absence of parameters that current guidelines do not consider (e.g. lifestyle) and (iii) absence of up-todate guidelines due to lengthy validation procedures. In this paper we present a novel method to build a CDSS that, besides integrating CIGs, stores experts’ knowledge to enrich the CDSS and provide best support to clinicians. The knowledge includes new evidence collected over time by the systematic usage of CDSSs.

Extended Reflexive Ontologies for the Generation of Clinical Recommendations

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.

Integrating Electronic Health Records in Clinical Decision Support Systems

Electronic Health Records (EHR) are systematic collections of digital health information about individual patients or populations. They provide readily access to the complete medical history of the patient, which is useful for decision-making activities. In this paper we focus on a secondary benefit of EHR: the reuse of the implicit knowledge embedded in it to improve the knowledge on the mechanisms of a disease and/or the effectiveness of the treatments. In fact, all such patient data registries stored in EHR reflect implicitly different clinical decisions made by the clinical professionals that participated in the assistance of patients (e.g. criteria followed during decision making, patient parameters taken into account, effect of the treatments prescribed). This work proposes a methodology that allows the management of EHR not only as data containers and information repositories, but also as clinical knowledge repositories. Moreover, we propose an architecture for the extraction of the knowledge from EHR. Such knowledge can be fed into a Clinical Decision Support System (CDSS), in a way that could render benefits for the development of innovations from clinicians, health managers and medical researchers.