Data Mining and Clinical Decision Support Systems

2007 ◽  
pp. 44-63 ◽  
Author(s):  
J. Michael Hardin ◽  
David C. Chhieng
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

2012 ◽  
pp. 84-110 ◽  
Author(s):  
Reza S. Kazemzadeh ◽  
Kamran Sartipi ◽  
Priya Jayaratna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Guideline ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Personnel

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.

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.

A Framework for Data and Mined Knowledge Interoperability in Clinical Decision Support Systems

2010 ◽  
Vol 5 (1) ◽  
pp. 37-60 ◽  
Author(s):  
Reza S. Kazemzadeh ◽  
Kamran Sartipi ◽  
Priya Jayaratna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Guideline ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Personnel

Due to reliance on human knowledge, the practice of medicine is subject to errors that endanger patients’ health and cause substantial financial loss to healthcare institutions. Computer-based decision support systems assist healthcare personnel to improve quality of clinical practice. Currently, most clinical guideline modeling languages represent decision-making knowledge in terms of basic logical expressions. In this paper, we focus on encoding, sharing, and using results of data mining analyses to influence decision making within Clinical Decision Support Systems. A knowledge management framework is proposed that addresses the issues of data and knowledge interoperability by adopting healthcare and data mining modeling standards. In a further step, data mining results are incorporated into a guideline-based decision support system. A prototype tool has been developed to provide an environment for clinical guideline authoring and execution. Also, three real world case studies have been presented, one of which is used as a running example throughout the paper.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

scholarly journals The acceptance of clinical decision support systems among clinicians in the treatment of neck and/or back pain

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S M Jansen-Kosterink ◽  
M Cabrita ◽  
I Flierman
Keyword(s):  
Back Pain ◽  
Decision Support ◽  
Focus Groups ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Online Questionnaire

Abstract Background Clinical Decision Support Systems (CDSSs) are computerized systems using case-based reasoning to assist clinicians in making clinical decisions. Despite the proven added value to public health, the implementation of CDSS clinical practice is scarce. Particularly, little is known about the acceptance of CDSS among clinicians. Within the Back-UP project (Project Number: H2020-SC1-2017-CNECT-2-777090) a CDSS is developed with prognostic models to improve the management of Neck and/or Low Back Pain (NLBP). Therefore, the aim of this study is to present the factors involved in the acceptance of CDSSs among clinicians. Methods To assess the acceptance of CDSSs among clinicians we conducted a mixed method analysis of questionnaires and focus groups. An online questionnaire with a low-fidelity prototype of a CDSS (TRL3) was sent to Dutch clinicians aimed to identify the factors influencing the acceptance of CDSSs (intention to use, perceived threat to professional autonomy, trusting believes and perceived usefulness). Next to this, two focus groups were conducted with clinicians addressing the general attitudes towards CDSSs, the factors determining the level of acceptance, and the conditions to facilitate use of CDSSs. Results A pilot-study of the online questionnaire is completed and the results of the large evaluation are expected spring 2020. Eight clinicians participated in two focus groups. After being introduced to various types of CDSSs, participants were positive about the value of CDSS in the care of NLBP. The clinicians agreed that the human touch in NLBP care must be preserved and that CDSSs must remain a supporting tool, and not a replacement of their role as professionals. Conclusions By identifying the factors hindering the acceptance of CDSSs we can draw implications for implementation of CDSSs in the treatment of NLBP.

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