Clinical Decision Support Systems

Dinevski, Dejan; Bele, Uroš; Šarenac, Tomislav; Rajkovič, Uroš; Šušteršič, Olga

doi:10.5772/25399

Cited by 17 publications

(12 citation statements)

References 28 publications

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“…They generally use statistical learning algorithms such as neural networks, genetic algorithms, etc. Where machines learn from examples in order to discover patterns and visible or hidden relationships between clinical data [8].…”

Section: Related Workmentioning

confidence: 99%

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Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Benmimoune

Hassani

Ghodous

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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Clinical decision support systems (CDSSs) have been hailed for their potential to reduce medical errors and improve the quality and safety of health care. CDSSs aim to assist clinicians in their decision making and take over some routines including diagnostic support. This paper presents a design of a generic medical platform to assist clinicians in their clinical reasoning process. Our platform aims to guide clinicians in the process of gathering relevant information and to help them in their decision making. It exploits two sources of knowledge namely: theoretical knowledge (clinical rules) and practical knowledge (clinical experiences) by relying on RuleBased Reasoning and Case-Based Reasoning, respectively. moreover, we utilize the semantic web and ontologies for knowledge modeling in order to reuse and to share knowledge.

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Section: Related Workmentioning

confidence: 99%

“…In the literature many studies have been conducted to categorize the CDSS, among which [6] and [8] have proposed to categorize CDSS into two mainly categories: Non-knowledgebased CDSSs and Knowledge-based CDSSs.…”

Section: Related Workmentioning

confidence: 99%

Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Benmimoune

Hassani

Ghodous

et al. 2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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“…Despite of many potentials generated by CDSS as we have previously discussed, the CDSS impact on daily clinical practice is less likely to produce the desired results [8], [9]. Therefore building a CDSS is a challenging process because theoretical knowledge is not sufficient in its implementation in the field.…”

Section: B Relevant Research: Assessment Of Cdss Applicationmentioning

confidence: 99%

“…Discussion of some of the above CDSS applications provides an understanding that computer-based CDSS has been applied since the early 2000s, even since the 1970s computer-based CDSS has been developed extensively [8]. Coeira in [8] also discusses several categories of computerbased CDSS based on the following objectives:…”

Section: B Relevant Research: Assessment Of Cdss Applicationmentioning

confidence: 99%

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Investigating Clinical Decision Support Systems Success Factors with Usability Testing

Tundjungsari¹,

Sofro²,

Sabiq³

et al. 2017

ijacsa

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Abstract-Clinical Decision Support Systems (CDSS) have been used widely since 2000s to improve the healthcare quality. CDSS can be utilized to support healthcare services as a tool to diagnose, predict, as well as to provide clinical interpretation, alert, and reminder. There are many researches of CDSS implementation on literatures but not many of them present the evidence of CDSS successful implementation. In spite of the potential use of CDSS, there are some researches that reveal the failures of CDSS implementation. This paper contributes to CDSS development by investigating and exploring CDSS success factors with usability testing. The testing involves participants from different types of backgrounds (physicians, IT developers, and students). The participants are being asked to experience three different CDSS to predict cardiovascular risk factors. The result of the research shows that involving different type of users give more insight to design process. It can be concluded that user center design is very critical to produce successful CDSS.

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Image Segmentation Method Based on Statistical Parameters of Homogeneous Data Set

Shkurat

Sulema

Suschuk-Sliusarenko

et al. 2019

Advances in Artificial Systems for Medicine and Education II

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Clinical Decision Support Systems

Cited by 17 publications

References 28 publications

Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Hybrid reasoning-based medical platform to assist clinicians in their clinical reasoning process

Investigating Clinical Decision Support Systems Success Factors with Usability Testing

Image Segmentation Method Based on Statistical Parameters of Homogeneous Data Set

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