Clinical Decision Support Systems Question and Answering

2018 ◽  
pp. 146-157 ◽  
Author(s):  
David José Murteira Mendes ◽  
Irene Pimenta Rodrigues ◽  
César Fonseca
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Discourse Representation ◽  
Discourse Representation Theory

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.

Clinical Decision Support Systems Question and Answering

2021 ◽  
pp. 543-554
Author(s):  
David José Murteira Mendes ◽  
Irene Pimenta Rodrigues ◽  
César Fonseca
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Discourse Representation ◽  
Discourse Representation Theory

A question answering system to help clinical practitioners in a cardiovascular healthcare environment to interface clinical decision support systems can be built by using an extended discourse representation structure, CIDERS, and an ontology framework, Ontology for General Clinical Practice. CIDERS is an extension of the well-known DRT (discourse representation theory) structures, intending to go beyond single text representation to embrace the general clinical history of a given patient represented in an ontology. The Ontology for General Clinical Practice improves the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty. The chapter shows the scientific and philosophical reasons of its present dual structure with a deeply expressive (SHOIN) terminological base (TBox) and a highly computable (EL++) assertions knowledge base (ABox). To be able to use the current reasoning techniques and methodologies, the authors made a thorough inventory of biomedical ontologies currently available in OWL2 format.

Clinical Decision Support Systems in Otolaryngology–Head and Neck Surgery: A State of the Art Review

2021 ◽  
pp. 019459982110045
Author(s):  
Taylor C. Standiford ◽  
Janice L. Farlow ◽  
Michael J. Brenner ◽  
Marisa L. Conte ◽  
Jeffrey E. Terrell
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Decision Support Systems ◽  
Head And Neck ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Neck Surgery ◽  
Information Technologists

Objective To offer practical, evidence-informed knowledge on clinical decision support systems (CDSSs) and their utility in improving care and reducing costs in otolaryngology–head and neck surgery. This primer on CDSSs introduces clinicians to both the capabilities and the limitations of this technology, reviews the literature on current state, and seeks to spur further progress in this area. Data Sources PubMed/MEDLINE, Embase, and Web of Science. Review Methods Scoping review of CDSS literature applicable to otolaryngology clinical practice. Investigators identified articles that incorporated knowledge-based computerized CDSSs to aid clinicians in decision making and workflow. Data extraction included level of evidence, Osheroff classification of CDSS intervention type, otolaryngology subspecialty or domain, and impact on provider performance or patient outcomes. Conclusions Of 3191 studies retrieved, 11 articles met formal inclusion criteria. CDSS interventions included guideline or protocols support (n = 8), forms and templates (n = 5), data presentation aids (n = 2), and reactive alerts, reference information, or order sets (all n = 1); 4 studies had multiple interventions. CDSS studies demonstrated effectiveness across diverse domains, including antibiotic stewardship, cancer survivorship, guideline adherence, data capture, cost reduction, and workflow. Implementing CDSSs often involved collaboration with health information technologists. Implications for Practice While the published literature on CDSSs in otolaryngology is finite, CDSS interventions are proliferating in clinical practice, with roles in preventing medical errors, streamlining workflows, and improving adherence to best practices for head and neck disorders. Clinicians may collaborate with information technologists and health systems scientists to develop, implement, and investigate the impact of CDSSs in otolaryngology.

Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease

Fuzzy Systems ◽  
2017 ◽  
pp. 184-201 ◽  
Author(s):  
Sidahmed Mokeddem ◽  
Baghdad Atmani
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Early Detection ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Decision

The use of data mining approaches in medicine and medical science has become necessary especially with the evolution of these approaches and their contributions medical decision support. Coronary artery disease (CAD) touches millions of people all over the world including a major portion in Algeria. However, much advancement has been done in medical science, but the early detection of CAD is still a challenge for prevention. Although, the early detection of CAD is a prevention challenge for clinicians. The subject of this paper is to propose new clinical decision support system (CDSS) for evaluating risk of CAD called CADSS. In this paper, the authors describe the characteristics of clinical decision support systems CDSSs for the diagnosis of CAD. The aim of this study is to explain the clinical contribution of CDSSs for medical decision-making and compare data mining techniques used for their implementation. Then, they describe their new fuzzy logic-based approach for detecting CAD at an early stage. Rules were extracted using a data mining technique and validated by experts, and the fuzzy expert system was used to handle the uncertainty present in the medical field. This work presents the main risk factors responsible for CAD and presents the designed CASS. The developed CADSS leads to 94.05% of accuracy, and its effectiveness was compared with different CDSS.

Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease

2016 ◽  
Vol 7 (3) ◽  
pp. 57-73 ◽  
Author(s):  
Sidahmed Mokeddem ◽  
Baghdad Atmani
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Early Detection ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Decision

The use of data mining approaches in medicine and medical science has become necessary especially with the evolution of these approaches and their contributions medical decision support. Coronary artery disease (CAD) touches millions of people all over the world including a major portion in Algeria. However, much advancement has been done in medical science, but the early detection of CAD is still a challenge for prevention. Although, the early detection of CAD is a prevention challenge for clinicians. The subject of this paper is to propose new clinical decision support system (CDSS) for evaluating risk of CAD called CADSS. In this paper, the authors describe the characteristics of clinical decision support systems CDSSs for the diagnosis of CAD. The aim of this study is to explain the clinical contribution of CDSSs for medical decision-making and compare data mining techniques used for their implementation. Then, they describe their new fuzzy logic-based approach for detecting CAD at an early stage. Rules were extracted using a data mining technique and validated by experts, and the fuzzy expert system was used to handle the uncertainty present in the medical field. This work presents the main risk factors responsible for CAD and presents the designed CASS. The developed CADSS leads to 94.05% of accuracy, and its effectiveness was compared with different CDSS.

scholarly journals Transparent Artificial Intelligence – A Conceptual Framework for Evaluating AI-based Clinical Decision Support Systems

2019 ◽  
Author(s):  
Tim Hahn ◽  
Ulrich Ebner-Priemer ◽  
Andreas Meyer-Lindenberg
Keyword(s):  
Artificial Intelligence ◽  
Clinical Practice ◽  
Decision Support ◽  
Conceptual Framework ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Daily Clinical Practice

With Artificial Intelligence (AI) technology advancing at a breathtaking speed – nearing application stage in many fields of medicine – the need for regulation ensuring quality, utility and security of the emerging AI-based clinical decision support systems becomes increasingly pressing. Here, we suggest a conceptual framework from which to derive requirements for building, validating, deploying, and managing AI-based systems in daily clinical practice.

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