Service Oriented Architecture for Clinical Decision Support: A Systematic Review and Future Directions

Loya, Salvador Rodriguez; Kawamoto, Kensaku; Chatwin, Chris; Huser, Vojtech

doi:10.1007/s10916-014-0140-z

Cited by 38 publications

(35 citation statements)

References 59 publications

(43 reference statements)

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“…This system produces alerts only for the very less number of precise results that are given whereas the CDSS produce massive number of alerts as it gives a wide range of recommendations. During the 6 month observation period, 12,189 patients were hospitalized in the six internal medicine departments studied and received 136,459 medication prescriptions [5]. Among these prescriptions, 30.1% triggered one or more CDSS alerts each month per department [5].…”

Section: Resultsmentioning

confidence: 99%

“…During the 6 month observation period, 12,189 patients were hospitalized in the six internal medicine departments studied and received 136,459 medication prescriptions [5]. Among these prescriptions, 30.1% triggered one or more CDSS alerts each month per department [5]. When a system produces a high volume of results the doctors tend to ignore or pay less attention to the warnings causing potentially critical alerts to be missed.…”

Section: Resultsmentioning

confidence: 99%

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Application of Perceptron Networks in Recommending Medical Diagnosis

Gullapalli¹,

Brungi²,

Gopichand³

2015

IJCA

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Artificial intelligence applications in medicine is the major and evolutionary topic in the technology world. Neural networks is an important branch of machine learning which is inspired from biological neural networks. Neural networks are useful in making proper decisions in rational environments with uncertainty. The neural networks perform better computation with high power with the help of the multiple interconnected neurons which act as processing elements. Decision theory along with probabilistic theory gives the good way to make the right decisions. Neural Network systems help in linking the health observations with the health knowledge database to take better decisions for good health. The ability of a neural network to learn by example can be implemented for taking decisions that would increase the rate of providing the better medical care facilities. This paper presents a novel methodology to implement supervised learning networks that is perceptron networks in medical diagnosis for providing such good decisions to the doctors in helping patients by also providing a good health care.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application of Perceptron Networks in Recommending Medical Diagnosis

Gullapalli¹,

Brungi²,

Gopichand³

2015

IJCA

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“…The are many research papers studying the use of serviceoriented architecture for medical software [11,12], and some of them mention service orchestration as a part of medical software, but this paper focuses on slightly different things, related to orchestration mechanism itself. Workflow composition or, in other words, automatic semanticbased orchestration scenario synthesis, makes is easier to extend existing functionality of the system.…”

Section: E-health Applicationmentioning

confidence: 99%

Automatic Service Orchestration for e-Health Application

Петренко¹,

Bulakh²

2019

Adv. sci. technol. eng. syst. j.

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This paper describes an architectural approach to the development of dynamic serviceoriented systems for e-Health using the service orchestration mechanism and semantic technologies. The main idea is the dynamic synthesis of the complex functionality required by user or by software agent. This idea should help to build and easily extend the applied loose-coupled systems without strict dependencies on concrete web services and their invocation details. A sample scenario of such dynamic orchestration is covered and analyzed, possible ways of further improvement of this approach are given.

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“…When CDSSs are created, using solutions to make use of these systems on a large scale can reduce the cost of creating and maintaining these systems and with their subscription, will prevent the production of multiple and island systems [4,5]. In recent years, one of the approaches used in combination with CDSSs for this purpose is the service-oriented architecture (SOA) approach [6][7][8][9]. In SOA as an integration strategy [10], decision support systems are available as a service to the client (individuals or systems) and provide bene ts such as facilitating knowledge storage, reducing costs, and improving agility [6].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, one of the approaches used in combination with CDSSs for this purpose is the service-oriented architecture (SOA) approach [6][7][8][9]. In SOA as an integration strategy [10], decision support systems are available as a service to the client (individuals or systems) and provide bene ts such as facilitating knowledge storage, reducing costs, and improving agility [6]. CDSS based Tele-monitoring systems and decision support telemedicine systems [11] are an emerging technology that is rapidly developing that bene t from the advantages of SOA.…”

Section: Introductionmentioning

confidence: 99%

Development of a Scalable Tele-Monitoring Platform based on the Clinical Decision Support Systems

Yazdani

Safdari

Sharifian

et al. 2020

Preprint

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Background: One of the most important types of information systems that play important role today in providing quality health care services are clinical decision support systems (CDSSs). These systems are effective in overcoming human resource constraint and intelligent analysis of information generated by Tele-monitoring systems. In spite of the many advantages of this architectures, these are single-purpose, meaning that only the CDSS of a disease is located on them. If we want to use the same model of architecture in the decision-making process of another disease, all the components of this architecture should be redevelopment with a new CDSS, which is time-consuming and costly. Due to the increasing demand for health information technology at low cost and mobile access in the health care industry, in this article, a scalable software platform(Patient Tele monitoring: PATEL) based on SOA for implementing and use different CDSSs on a common platform, for use in Tele-monitoring Systems, was created.Implementation: To develop PATEL platform, the component-based software development approach and hybrid programming approach to implementing various components used. In the evaluation phase of the proposed platform, the case study, accuracy and performance evaluation (transmission delays, patient data fetch, parsing overhead and inference time) used.Results: The results of the case study evaluation confirmed the scalability and interoperability between CDSSs on the platform. Based on performance evaluation, the proposed platform has responded to 89% of the requests in less than one second. Also, based on accuracy evaluation, the platform presented in this article was successful in diagnosing 91.6% of the cases.Conclusion: The proposed platform can support CDSSs of various diseases simultaneously and provides the necessary scalability to add a new CDSS. Tele-monitoring systems will be capable of service by connecting to this platform. Using this infrastructure is expected to be a lot of duplication in the implementation of tele-monitoring systems based CDSSs will be reduced.

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Service Oriented Architecture for Clinical Decision Support: A Systematic Review and Future Directions

Cited by 38 publications

References 59 publications

Application of Perceptron Networks in Recommending Medical Diagnosis

Application of Perceptron Networks in Recommending Medical Diagnosis

Automatic Service Orchestration for e-Health Application

Development of a Scalable Tele-Monitoring Platform based on the Clinical Decision Support Systems

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