The Implementation of Clinical Decision Support System: A Case Study in Saudi Arabia

Alqahtani, Sahar S.; Alshahri, Sabah; Almaleh, Ahood I.; Aljohani, Naif Radi; Nadeem, Farrukh

doi:10.5815/ijitcs.2016.08.03

Cited by 14 publications

(13 citation statements)

References 31 publications

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“…The AI is a class of PC algorithm that can comprehend human types of activities and insightfully use them to settle on decision or expect what's to come. The AI algorithms vigorously depend on the accessible information from past perceptions, which incorporate data given by the doctor, staff, drug store and other medical care people (12). CDSS controlled by AI Machine Learning (ML) ideas have been a long quest for future-arranged specialists, patient integrity specialists, information researchers, and operational masters the same.…”

Section: Clinical Decision-makingmentioning

confidence: 99%

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Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Salah

Ahmed

2021

Baghdad Sci.J

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Automated clinical decision support system (CDSS) acts as new paradigm in medical services today. CDSSs are utilized to increment specialists (doctors) in their perplexing decision-making. Along these lines, a reasonable decision support system is built up dependent on doctors' knowledge and data mining derivation framework so as to help with the interest the board in the medical care gracefully to control the Corona Virus Disease (COVID-19) virus pandemic and, generally, to determine the class of infection and to provide a suitable protocol treatment depending on the symptoms of patient. Firstly, it needs to determine the three early symptoms of COVID-19 pandemic criteria (fever, tiredness, dry cough and breathing difficulty) used to diagnose the person being infected by COVID-19 virus or not. Secondly, this approach divides the infected peoples into four classes, based on their immune system risk level (very high degree, high degree, mild degree, and normal), and using two indices of age and current health status like diabetes, heart disorders, or hypertension. Where, these people are graded and expected to comply with their class regulations. There are six important COVID-19 virus infections of different classes that should receive immediate health care to save their lives. When the test is positive, the patient age is considered to choose one of the six classifications depending on the patient symptoms to provide him the suitable care as one of the four types of suggested treatment protocol of COVID-19 virus infection in COVID-19 DSS application. Finally, a report of all information about any classification case of COVID-19 infection is printed where this report includes the status of patient (infection level) and the prevention protocol. Later, the program sends the report to the control centre (medical expert) containing the information. In this paper, it was suggested the use of C4.5 Algorithm for decision tree.

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Section: Clinical Decision-makingmentioning

confidence: 99%

“…The Internist-I was utilizing a treeorganized data base that connected side effects with sicknesses. The assessment of the framework uncovered that it was not adequately dependable for clinical application (12). Now, a few investigations have been accounted for that have zeroed in on clinical determination.…”

Section: Related Workmentioning

confidence: 99%

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Salah

Ahmed

2021

Baghdad Sci.J

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“…In terms of CDSS, two studies were conducted to assess its effectiveness. Alqahtani et al (2016) summarized the challenges of the Clinical Decision Support System (CDSS) and its effectiveness to improve clinical practice. The study concluded that the healthcare sector is in dire need to increase the quality of patients' care and improve clinical practices by adopting CDSS (Alqahtani et al, 2016).…”

Section: Important Topics Covered By the Previous Work About Saudi Amentioning

confidence: 99%

“…Electronic health (e-health) is the adoption of information and communication technologies for health delivery. Around the world, countries are embracing e-health as a way of transforming healthcare delivery (Alqahtani et al, 2016). Remarkably, in Saudi Arabia, e-health technology has been implemented in the different healthcare facilities to improve the quality and accessibility to healthcare services.…”

Section: Introductionmentioning

confidence: 99%

Electronic-health in Saudi Arabia: A review

Alshammari¹

2021

Int. j. adv. appl. sci.

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Different nations across the world are attempting to embrace e-health as a platform for transforming healthcare delivery. This review focuses on the state of e-health in KSA, including studies on trends and current technologies employed in e-health. It has been determined in this paper that even though e-health is being implemented in Saudi Arabia, the rate of implementation is quite slow, and it seems to lag behind the rate that was desired and envisioned. Additionally, some challenges hinder the adoption and effective implementation of e-health, including lack of awareness, lack of talented workforce, and ineffective planning. It has been determined that if these challenges are overcome, then e-health can be adapted efficiently.

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“…The healthcare sector is showing inclination towards restructuring of healthcare systems to harmonize with technological innovations and adopting decision support system in routine clinical practices (Alqahtani et al, 2016). Medical experts are now demanding for knowledge intensive Computational Intelligence (CI) and Knowledge Based System(KBS) in finding solutions to medical problems (Adetumbi, Oguntimilehin and Falaki, 2012).…”

Section: Introductionmentioning

confidence: 99%

Symptomatic and Climatic Based Malaria Threat Detection Using Multilevel Thresholding FeedForward Neural Network

A.¹,

R.²

2017

IJITCS

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Abstract-Recent worldwide medical research is focusing on new intelligence approaches for diagnosis of various infections. The sporadic occurrence of malaria diseases in human has pushed the need to develop computational approaches for its diagnoses. Most existing conventional malaria models for classification problems examine the dynamics of asymptomatic and morphological characteristics of malaria parasite in the thick blood smear, but this study examine the symptomatic characteristics of malaria parasite combined with effects of climatic factor which is a great determinant of malaria severity. The need to predict the occurrence of malaria disease and its outbreak will be helpful to take appropriate actions by individuals, World Health Organizations and Government Agencies and its devastating impact will be reduced. This paper proposed Feed-Forward Back-Propagation (FF_BP) Neural Network model to determine the rate of malaria transmission. Monthly averages of climatic factors; rainfall, temperature and relative humidity with monthly malaria incidences were used as input variables. An optimum threshold value of 0.7100 with classification accuracy 87.56%, sensitivity 96.67% and specificity 76.67% and mean square error of 0.100 were achieved. While, the model malaria threat detection rate was 87.56%, positive predictive value was 89.23%, negative predictive value was 92.00% and the standard deviation is 2.533. The statistical analysis of Feed-Forward BackPropagation Neural Network model was conducted and its results were compared with other existing models to check its robustness and viability.

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The Implementation of Clinical Decision Support System: A Case Study in Saudi Arabia

Cited by 14 publications

References 31 publications

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Coronavirus Disease Diagnosis, Care and Prevention (COVID-19) Based on Decision Support System

Electronic-health in Saudi Arabia: A review

Symptomatic and Climatic Based Malaria Threat Detection Using Multilevel Thresholding FeedForward Neural Network

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