Adjustment of Medical Observations Influenced by Emotional State

2018 ◽  
Vol 9 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Firas Zekri ◽  
Afef Samet Ellouze ◽  
Rafik Bouaziz
Keyword(s):  
Decision Support ◽  
Fuzzy Systems ◽  
Emotional State ◽  
Strong Dependence ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Observation ◽  
Using Data

Research in neurophysiology and neuropsychology have established a strong dependence between emotion, subjectivity and decision-making. Otherwise, medical observations are used as one of the main inputs of clinical decision support systems (CDSS) which are designed to support patients with chronic progressive diseases. However, these observations are influenced when confronted with a critical emotional state and they are likely to be subjective. To generate efficient results, CDSS must bring these subjective observations closer to the reality by using data describing the observer's emotional state. To solve this issue, the authors of this article propose to identify the dependency relationship between observations and emotions. Then they provide a solution that moderates the patient and caregivers' observations within a medical decision support system, so that it can generate efficient results. Finally, they propose two fuzzy systems to adjust the influence of emotional state on medical observation. These two systems make the medical observation closer to the current condition of the patient.

Taking Inventory of Medical Decision Support Software Development

1993 ◽  
Vol 32 (01) ◽  
pp. 9-11 ◽  
Author(s):  
R. A. Miller
Keyword(s):  
Decision Support ◽  
Software Development ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Support Software

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.

scholarly journals Medical Decision Support Systems in Africa

2010 ◽  
Vol 19 (01) ◽  
pp. 47-54 ◽  
Author(s):  
C. O. Bagayoko ◽  
A. Geissbuhler ◽  
G. Bediang
Keyword(s):  
Public Health ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Sub Saharan Africa ◽  
Medical Decision ◽  
Medical Decision Support ◽  
Medical Decision Support Systems

Summary Objective: To present an overview of the current state of computerbased medical decision support systems in Africa in the areas of public health, patient care, and consumer support. Methods: Scientific and gray literature reviews complemented by expert interviews. Results: Various domains of decision support are developed and deployed in Sub-Saharan Africa: public health information systems, clinical decision-support systems, and patient-centred decisionsupport systems. Conclusions: Until recently, most of these systems have been deployed by international organizations without a real ownership policy entrusted to the African stakeholders. Many of these endeavours have remained or ceased at the experimentation stage. The multiplicity of organizations has led to the deployment of fragmented systems causing serious interoperability problems. In addition to basic infrastructures, these studies also highlight the importance of good organization, training and support, as key to the success and sustainability of these decision support systems.

The Good, the Bad and the Ugly in App Diagnosis: Outcomes and Implications by Example

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 38-38
Author(s):  
Sorana D. Bolboacă ◽  
◽  
Adriana Elena Bulboacă ◽  
◽  
◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision ◽  
Medical Decision ◽  
Normal Body Mass Index ◽  
Disk Herniation ◽  
Scientific Evaluation ◽  
Medical Decision Support ◽  
Lower Back ◽  
Clinical Scenarios ◽  
Medical Decision Support Systems

"The Clinical Decision Support (CDS), a form of artificial intelligence (AI), consider physician expertise and cognitive function along with patient’s data as the input and case-specific medical decision as an output. The improvements in physician’s performances when using a CDS ranges from 13% to 68%. The AI applications are of large interest nowadays, and a lot of effort is also put in the development of IT applications in healthcare. Medical decision support systems for non-medical staff users (MDSS-NMSF) as phone applications are nowadays available on the market. A MDSS-NMSF app is generally not accompanied by a scientific evaluation of the performances, even if they are freely available or not. Two clinical scenarios were created, and Doctor31 retrieved the diagnosis decisions. First scenario: man, 29 years old, and three symptoms: dysphagia, weight loss (normal body mass index), and tiredness. Second scenario: women, 47 years old with L5-S1 disk herniation, abnormal anti-TPO antibodies, lower back pain (burning sensations), constipation, and tiredness. The outcome possible effects and implications, as well as vulnerabilities induced on the used, are highlighted and discussed. "

Clinical Decision Making by Emergency Room Physicians and Residents

2008 ◽  
pp. 217-223
Author(s):  
Ken J. Farion ◽  
Michael J. Hine ◽  
Wojtek Michalowski ◽  
Szymon Wilk
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Clinical Decision Making ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Medical Community ◽  
Medical Decision ◽  
Best Interest ◽  
Complex Process

Clinical decision-making is a complex process that is reliant on accurate and timely information. Clinicians are dependent (or should be dependent) on massive amounts of information and knowledge to make decisions that are in the best interest of the patient. Increasingly, information technology (IT) solutions are being used as a knowledge transfer mechanism to ensure that clinicians have access to appropriate knowledge sources to support and facilitate medical decision making. One particular class of IT that the medical community is showing increased interest in is clinical decision support systems (CDSSs).

Decision-Making and Decision Support in Acute Care

2013 ◽  
pp. 1-18
Author(s):  
Brett W. Taylor
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support Systems ◽  
Evaluation Process ◽  
Clinical Decision ◽  
Medical Decision ◽  
Information Tools ◽  
Novices And Experts ◽  
And Function ◽  
Medical Choice

Clinical Decision Support Systems (CDSS) are information tools intended to optimize medical choice, promising better patient outcomes, faster care, reduced resource expenditure, or some combination of all three. Clinical trials of CDSS have provided only insipid evidence of benefit to date. This chapter reviews the theory of medical decision-decision making, identifying the different decision support needs of novices and experts, and demonstrates that discipline, objective and setting, and affect of the nature of support that is required. A discussion on categorization attempts to provide metrics by which systems can be compared and evaluated, in particular with regard to decision support mechanics and function. Throughout, the common theme is the placement of clinical decision makers at the center of the design or evaluation process.

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.

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