A lightweight acquisition of expert rules for interoperable clinical decision support systems

Canovas-Segura, Bernardo; Morales, Antonio; Palácios, Francisco

doi:10.1016/j.knosys.2019.01.007

Cited by 16 publications

(9 citation statements)

References 37 publications

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“…WASPSS uses artificial intelligence techniques to provide proper support for ASP teams. In particular, WASPSS includes a knowledge module to incorporate clinical literature and daily practice knowledge [33,37,42]. To this end, the module implements a rule-based reasoning architecture composed of a knowledge base and a rulebased engine.…”

Section: System Architecture Overviewmentioning

confidence: 99%

“…WASPSS combines production rules with ontologies in order to incorporate the EUCAST rules in its knowledge base. Production rules are used to model most of the EUCAST knowledge, while ontologies are needed to model the hierarchical relationships of antibiotics and bacteria [36,37]. This knowledge can be useful for many tasks such as the detection of incoherencies in laboratory results (e.g., bacteria that are found susceptible to an antibiotic to which they should be intrinsically resistant according to the EUCAST rules) and the detection of possible therapy failure because the infecting agent is found as resistant to all the antibiotics currently administered to the patient.…”

Section: Incorporation Of Clinical Knowledgementioning

confidence: 99%

“…The use of WASPSS in several hospitals offers us the opportunity to combine efforts among different ASP teams to improve the global outcomes in antimicrobial stewardship. On the other hand, it raises the problems of the interchange of knowledge among hospitals [37] and the need for dealing with a huge amount of data, which may require the adaptation of WASPSS to a big data environment [44].…”

Section: Future Workmentioning

confidence: 99%

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WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

Canovas-Segura¹,

Morales²,

Juárez³

et al. 2021

Recent Advances in Digital System Diagnosis and Management of Healthcare

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The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multiuser web interface, personalized for each role of the ASP team.

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Section: System Architecture Overviewmentioning

confidence: 99%

Section: Incorporation Of Clinical Knowledgementioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

See 1 more Smart Citation

WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

Canovas-Segura¹,

Morales²,

Juárez³

et al. 2021

Recent Advances in Digital System Diagnosis and Management of Healthcare

Self Cite

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“…In order to address the problem that more detailed knowledge might be neglected during literature review, the proposed framework verified the validity and efficiency of domain ontology. Cánovas‐Segura et al [ 37 ] effectively combined ontology with external knowledge sources and data sets and constructed an ontology model in a top‐down and bottom‐up way to realize knowledge communication between systems. Huang et al [ 38 ] proposed a data‐driven ontology generation and evolution framework that mapped multi‐source heterogeneous data into a unified data instance.…”

Section: Related Workmentioning

confidence: 99%

An ontology‐based procedure knowledge framework for the process industry

Cao

Zhu

2020

Can J Chem Eng

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Process industry enterprises rely heavily on expert experience in production, and expert knowledge is stored in multiple formats like, pictures, texts, videos, etc. Several isolated islands of information or knowledge are formed, and they are difficult to store, share, and expand upon. Therefore, the same procedure appears multiple times in different application scenarios, such as optimization scheduling, optimization operation, or fault diagnosis. If redundant knowledge is paid too much attention, decision-makers will not be able to comprehensively consider business knowledge. Thus, a novel framework is proposed for the process industry to manage procedure knowledge. This paper first divides the detailed procedure of process plants into four layers, that is, raw materials, intermediate materials, operation, and products based on the P-graph theory, which involves virtual hierarchies, nodes, and operations. Second, the domain ontology-based procedure knowledge model is constructed. Finally, two experimental cases, the Tennessee Eastman (TE) and the ethylene production process, are studied to verify the procedure knowledge framework (PKF). The PKF provides a useful foundation for the subsequent construction of a superstructure model based on the P-graph theory and is different from the previous industrial process industrial ontology model. The PKF has a theoretical basis for simulation calculation and makes future work based on PKF more accurate and interpretable.

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“…Ontologies provide the basis to establish an explicit formal concept specification in a specific domain, allowing the development of relationships between these concepts and the reuse and integration of domain knowledge [10]. In the hospital domain, ontologies provide information associated with a wealth of knowledge about clinical decisions [3], medication [2], diagnoses [19], medical records [11], and so on.…”

Section: Introductionmentioning

confidence: 99%

Towards an Ontology to Support Decision-making in Hospital Bed Allocation (S)

Engelmann¹,

Couto²,

Gabriel³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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Using advanced technologies is imperative to support quick decision-making in a hospital, where people work with complex and critical processes. An example of a complex and very important task is to make the best decision about in which room and bed a patient should be admitted to hospital, considering the patient needs, characteristics, and available resources. In this case, bad decisions can even compromise patient health. With this study, we aim to facilitate patient-related decisions related to bed allocation, based on an ontology. To do so, we developed an ontology that takes patients' information into account to help health professionals decide where to allocate them. Our main contribution is an ontology, with classes, relationships, individuals, and rules to be used in specific scenarios. Additionally, we exemplify some scenarios in which the rules could be applied.Index Terms-Ontology, health care, hospital bed allocation.

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A lightweight acquisition of expert rules for interoperable clinical decision support systems

Cited by 16 publications

References 37 publications

WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

An ontology‐based procedure knowledge framework for the process industry

Towards an Ontology to Support Decision-making in Hospital Bed Allocation (S)

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