Medical Case Based Reasoning Frameworks

2016 ◽  
Vol 8 (3) ◽  
pp. 31-62 ◽  
Author(s):  
Shaker El-Sappagh ◽  
Mohammed Mahfouz Elmogy
Keyword(s):  
English Language ◽  
Diabetes Management ◽  
Critical Evaluation ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Case Based Reasoning ◽  
Medical Studies ◽  
Medical Systems ◽  
Medical Domain ◽  
Case Based

Case-Based Reasoning (CBR) is one of the most suitable AI techniques for building clinical decision support systems. Medical domain complexity introduces many challenges for building these systems. Building the systems' knowledge base from the Electronic Health Record (EHR), the encoding of case-base knowledge with standard medical ontology, and the handling of vague data are examples of these challenges. Although several advantages of using CBR in medicine have been identified, there are no real systems acceptable to physicians. This systematic review examines the current state of CBR and its limitations in the medical domain, especially for diabetes mellitus. The critical evaluation of the status of diabetes CBR systems presents unique opportunities for improving these systems. The literature review covers most of the English language studies extracted from relevant databases by using search terms relating CBR, ontology, Fuzzy, and standard terminology concepts. The authors identify 38 articles published between 1999 and 15 January 2015, which represent original researches in CBR domain. The study includes 15 (39.5%) non-medical studies and 23 (60.5%) medical studies with ~22% for diabetes CBR. A list of 18 integrated evaluation metrics has been proposed and used in the analysis. The results show that the non-medical CBR systems achieved higher advances (50%) than medical systems (42.9%). In addition, the diabetes management CBR systems achieve the lowest advances (21.4%) compared to other systems. These shortages explain the question “why CBR paradigm are not fully utilized in the commercial medical systems?” As a result, there is a distinct need for more comprehensive enhancements in clinical CBR especially diabetes systems.

Medical Case Based Reasoning Frameworks

2020 ◽  
pp. 516-552
Author(s):  
Shaker El-Sappagh ◽  
Mohammed Mahfouz Elmogy
Keyword(s):  
Diabetes Management ◽  
Critical Evaluation ◽  
Case Based Reasoning ◽  
Medical Studies ◽  
Medical Systems ◽  
Language Studies ◽  
The Status ◽  
Medical Domain ◽  
Vague Data ◽  
Case Based

Case-Based Reasoning (CBR) is one of the most suitable AI techniques for building clinical decision support systems. Medical domain complexity introduces many challenges for building these systems. Building the systems' knowledge base from the Electronic Health Record (EHR), the encoding of case-base knowledge with standard medical ontology, and the handling of vague data are examples of these challenges. Although several advantages of using CBR in medicine have been identified, there are no real systems acceptable to physicians. This systematic review examines the current state of CBR and its limitations in the medical domain, especially for diabetes mellitus. The critical evaluation of the status of diabetes CBR systems presents unique opportunities for improving these systems. The literature review covers most of the English language studies extracted from relevant databases by using search terms relating CBR, ontology, Fuzzy, and standard terminology concepts. The authors identify 38 articles published between 1999 and 15 January 2015, which represent original researches in CBR domain. The study includes 15 (39.5%) non-medical studies and 23 (60.5%) medical studies with ~22% for diabetes CBR. A list of 18 integrated evaluation metrics has been proposed and used in the analysis. The results show that the non-medical CBR systems achieved higher advances (50%) than medical systems (42.9%). In addition, the diabetes management CBR systems achieve the lowest advances (21.4%) compared to other systems. These shortages explain the question “why CBR paradigm are not fully utilized in the commercial medical systems?” As a result, there is a distinct need for more comprehensive enhancements in clinical CBR especially diabetes systems.

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.

Medical applications in case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 289-292 ◽  
Author(s):  
ALEC HOLT ◽  
ISABELLE BICHINDARITZ ◽  
RAINER SCHMIDT ◽  
PETRA PERNER
Keyword(s):  
Medical Applications ◽  
Case Based Reasoning ◽  
Field Systems ◽  
Medical Domain ◽  
Case Based

This commentary summarizes case-based reasoning research applied in the medical domain. In this commentary the term ‘medical’ is used in an all-encompassing manner. It comprises all aspects of health, for example, from diagnosis to nutrition planning. This article provides references to researchers in the field, systems, workshops, and landmark publications.

scholarly journals Smart Diabetic Screening and Managing Software, A Novel Decision Support System

2018 ◽  
Vol 8 (3Sep) ◽  
Author(s):  
M Ghoddusi Johari ◽  
M H Dabaghmanesh ◽  
H Zare ◽  
A R Safaeian ◽  
Gh Abdollahifard
Keyword(s):  
Diabetes Mellitus ◽  
Decision Support ◽  
Chronic Disease ◽  
Decision Support System ◽  
Support System ◽  
Diabetes Care ◽  
Diabetes Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Diabetic Screening

Background: Diabetes is a serious chronic disease, and its increasing prevalence is a global concern. If diabetes mellitus is left untreated, poor control of blood glucose may cause long-term complications. A big challenge encountered by clinicians is the clinical management of diabetes. Many IT-based interventions such ad CDSS have been made to improve the adherence to the standard care for chronic diseases.Objective: The aim of this study is to establish a decision support system of diabetes management based on diabetes care guidelines in order to reduce medical errors and increase adherence to guidelines.Materials and Methods: To start the process, at first the existing guidelines in the field of diabetes mellitus such as ADA 2017 and AACE guideline 2017 were reviewed, and accordingly, flowcharts and algorithms for screening and managing of diabetes were designed. Then, it was passed on to the information technology team to design software.Results: The most significant outcome of this research was to establish a smart diabetic screening and managing software, which is an important stride to promote patients' health status, control diabetes and save patients' information as an important and reliable source. Conclusion: Health care technologies have the potential to improve the quality of diabetes care through IT-based intervention, such as clinical decision support systems. In a chronic disease like diabetes, the critical component is the disease management. The advantages of this web-based system are on-time registration, reports of diabetic prevalence, uncontrolled diabetes, diabetic complications and reducing the rate of mismanagement of diabetes, so that it helps the physicians in order to manage the patients in a better way.

scholarly journals Clinical Decision Support Systems

2018 ◽  
pp. 153-169 ◽  
Author(s):  
A. T. M. Wasylewicz ◽  
A. M. J. W. Scheepers-Hoeks
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Data Science ◽  
Support Systems ◽  
Critical Evaluation ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
High Quality ◽  
Normal Limits

AbstractClinical decision support (CDS) includes a variety of tools and interventions computerized as well as non- computerized. High-quality clinical decision support systems (CDSS), computerized CDS, are essential to achieve the full benefits of electronic health records and computerized physician order entry. A CDSS can take into account all data available in the EHR making it possible to notice changes outside the scope of the professional and notice changes specific for a certain patient, within normal limits. However, to use of CDSS in practice, it is important to understand the basic requirements of these systems.This chapter shows in what way CDSS can support the use of clinical data science in daily clinical practice. Moreover, it explains what types of CDSS are available and how such systems can be used. However, to achieve high-quality CDSS which is effective in use requires thoughtful design, implementation and critical evaluation. Therefore, challenges surrounding implementation of a CDSS are discussed, as well as a strategies to develop and validate CDSS.

Sign in / Sign up

Export Citation Format

Share Document