Extended Clinical Discourse Representation Structure for Controlled Natural Language Clinical Decision Support Systems

2015 ◽  
Vol 4 (2) ◽  
pp. 1-11 ◽  
Author(s):  
David José Murteira Mendes ◽  
Irene Pimenta Rodrigues ◽  
Carlos F Baeta ◽  
Carlos Solano-Rodriguez
Keyword(s):  
Medical Science ◽  
Clinical Decision Support Systems ◽  
Clinical History ◽  
Clinical Decision ◽  
Discourse Representation ◽  
Discourse Representation Theory ◽  
Single Text ◽  
Discourse Representation Structure ◽  
History Of ◽  
Representation Structure

To support an end to end Question and Answering system to help the clinical practitioners in a cardiovascular healthcare environment, an extended discourse representation structure CIDERS is introduced. This extension of the well-known DRT (Discourse Representation Theory) structures, go beyond single text representation extending them to embrace the general clinical history of a given patient. Introduced is a proposed and developed ontology framework, Ontology for General Clinical Practice, enhancing the currently available state-of-the-art ontologies for medical science and for the cardiovascular specialty, It's shown 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).

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.

Intelligent Techniques Inspired by Nature and Used in Biomedical Engineering

2018 ◽  
pp. 51-77
Author(s):  
Omer Deperlioglu
Keyword(s):  
Medical Information ◽  
Intelligent System ◽  
Medical Science ◽  
Healthcare Providers ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Patient Specific ◽  
Real Problem ◽  
History Of ◽  
The Right

Managing medical information and knowledge is becoming an increasing problem for healthcare professionals. Medical science that contains ever-increasing amounts of knowledge, such as the medical history of a patient, medical data about diseases, diagnosis and treatment methods, should be necessarily a science of information. The real problem faced by patients and healthcare providers is finding and using relevant knowledge at the right time. In this context, in the middle of 1950s, intelligent computer systems, called clinical decision support systems (CDSS), were introduced as a new concept. CDSS is defined as an active intelligent system that can help medical experts to make decisions by taking specific recommendations. Also, it provides decisions based on resolving patient-specific information and related medical truths. The objective of this chapter is to focus on these systems and explain relations with the field of artificial intelligence methods, approaches, or techniques in this manner.

Intelligent Techniques Inspired by Nature and Used in Biomedical Engineering

Biotechnology ◽  
2019 ◽  
pp. 666-692
Author(s):  
Omer Deperlioglu
Keyword(s):  
Medical Information ◽  
Intelligent System ◽  
Medical Science ◽  
Healthcare Providers ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Patient Specific ◽  
Real Problem ◽  
History Of ◽  
The Right

Managing medical information and knowledge is becoming an increasing problem for healthcare professionals. Medical science that contains ever-increasing amounts of knowledge, such as the medical history of a patient, medical data about diseases, diagnosis and treatment methods, should be necessarily a science of information. The real problem faced by patients and healthcare providers is finding and using relevant knowledge at the right time. In this context, in the middle of 1950s, intelligent computer systems, called clinical decision support systems (CDSS), were introduced as a new concept. CDSS is defined as an active intelligent system that can help medical experts to make decisions by taking specific recommendations. Also, it provides decisions based on resolving patient-specific information and related medical truths. The objective of this chapter is to focus on these systems and explain relations with the field of artificial intelligence methods, approaches, or techniques in this manner.

The measurement of process and the role of information technology

1993 ◽  
Vol 101 ◽  
pp. 31-53
Author(s):  
M. P. Ryan ◽  
W. Dodd
Keyword(s):  
Information Technology ◽  
Quality Assurance ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Data Sets ◽  
Process Of Care ◽  
Operational Systems ◽  
History Of ◽  
Computer Based ◽  
English Speaking

SynopsisProcess of care is the most immediate, relevant and susceptible to improvement of Donabedian's three elements of quality assurance. To place the study of process in context, the history of quality assurance in English-speaking countries is reviewed, with particular emphasis on the U.S.A. A range of methods are examined in detail and examples are provided to illustrate their strengths and weaknesses. Particular attention is paid to the use of explicit and implicit criteria. The importance of information technology in monitoring the process of care cannot be over-stated. Limited audit is possible with manual records but all substantial projects require computer support. The value of capturing data from operational systems rather than from dedicated projects is emphasised. Attention is drawn to the key importance of structured records and minimum data sets; these allow information to be pooled and process studies to be generalised. Examples are given of quality assurance projects which have used information technology. Finally potential future developments are reviewed with particular reference to clinical guidelines and computer-based clinical decision support systems.

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.

A Clinical Decision Support System for Chronic Venous Insufficiency

2017 ◽  
Author(s):  
Chiara Calamanti ◽  
Annalisa Cenci ◽  
Michele Bernardini ◽  
Emanuele Frontoni ◽  
Primo Zingaretti
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Chronic Venous Insufficiency ◽  
Venous Insufficiency ◽  
Clinical Decision Making ◽  
Clinical Performance ◽  
Clinical Decision Support Systems ◽  
Clinical History ◽  
Clinical Decision ◽  
Decision Quality

Earlier diagnosis plays a pivotal role in clinical applications, since it can strongly reduce the incidence and impact of many diseases and, consequently, the reduction of health care costs. This last aspect depends strongly from right therapy prescriptions, especially when there are various opportunities. Within this context, Clinical Decision Support Systems (CDSS) could bring several benefits. In this paper, we propose a CDSS with the aim of improving the clinician practice based on recommendations, assessment of the patient and screening of patients with risk factors to prevent chronic venous insufficiency (CVI) complications. The proposed CDSS is implemented in the Nu.Sa. cloud system, which involves thousands of italian General Practitioners (GPs) collecting data (EHR data, personal data, patient’s medical history) from millions of patients. The proposed architecture is designed to collect data from a distributed scenario where GPs are collecting clinical history and pharmacy or second level hospitals gather data from medical devices connected to the cloud over a standard data architecture. We show that exploiting the integration of the medical device VenoScreen Plus with the patient EHR, this CDSS is capable to improve preventive care, to enhance clinical performance, to influence clinical decision making and to significantly improve the decision quality levering on data driven approach.

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.

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