Investigating an ontology-based approach for Big Data analysis of inter-dependent medical and oral health conditions

Shah, Tejal; Rabhi, Fethi A.; Ray, Pradeep

doi:10.1007/s10586-014-0406-8

Cited by 44 publications

(30 citation statements)

References 43 publications

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“…Separate display of information sources (Thyvalikakath et al, 2012) Integrate medical and dental history (Rudman et al, 2010;Shah et al, 2015) Simple, static and non-learning (Dympna et al, 2014) Intelligent and personalised (Horgan et al, 2014) Perceived as not useful and time consuming Efficient searching, retrieval algorithm and user-friendly HCI Limited scope (Smart and Sadraie, 2012) Interoperability and accessibility (Garcia et al, 2013) Lee et al (2012) has researched the optimal selection of dental implant abutments. A fuzzy cognitive map is used to contain rules and expert domain knowledge from both the dentist using the system as well as domain experts from implant manufacturers.…”

Section: Current Clinical Dss Design Features Expected Design Featuresmentioning

confidence: 99%

“…Compared to other fields such as education and finance, velocity and variety of data generated in healthcare is much more significant, with Figure 7 illustrating a big data heat map covering these domains, adapted from Shah et al (2015).…”

Section: Big Datamentioning

confidence: 99%

“…From the heat map, it is evident that the quantity and expected speed of processing, analysing and distributing of information in healthcare will "bring the potential to discover new knowledge that can improve work practices and produce better outcomes" (Shah et al, 2015). This is particularly true in the dental clinic where the dentist needs to consider information from intra-oral images, 3D images, unstructured clinical notes and the patient's profile in real-time at point-of-care before deciding on a personalised treatment plan.…”

Section: Big Datamentioning

confidence: 99%

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Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

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While most dental clinicians use some sort of information system, they are involved with administrative functions, despite the advisory potential of some of these systems. This paper outlines some current decision support systems (DSS) and the common barriers facing dentists in adopting them within their workflow. These barriers include lack of perceived usefulness, complicated social and economic factors, and the difficulty for users to interpret the advice given by the system. A survey of current systems found that although there are systems that suggest treatment options, there is no real-time integration with other knowledge bases. Additionally, advice on drug prescription at point-of-care is absent from such systems, which is a significant omission, in consideration of the fact that disease management and drug prescription are common in the workflow of a dentist. This paper also addresses future trends in the research and development of dental clinical DSS, with specific emphasis on big data, standards and privacy issues to fulfil the vision of a robust, user-friendly and scalable personalised DSS for dentists. The findings of this study will offer strategies in design, research and development of a DSS with sufficient perceived usefulness to attract adoption and integration by dentists within their routine clinical workflow, thus resulting in better health outcomes for patients and increased productivity for the clinic.

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Section: Current Clinical Dss Design Features Expected Design Featuresmentioning

confidence: 99%

Section: Big Datamentioning

confidence: 99%

Section: Big Datamentioning

confidence: 99%

See 1 more Smart Citation

Decision support systems for adoption in dental clinics: A survey

Goh

Tao

et al. 2016

Knowledge-Based Systems

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“…These solutions are focused on semantic technologies for aggregating and collating data, both structured and unstructured [13]. The cloud middleware layer can be developed based on three sub-layers, as follows: local cloud components on slave sensors node, edge cloud components on master gateway nodes and central cloud components on application / datastore server nodes.…”

Section: Big Data Processing For Convergent Cloud Iot E-health Applicmentioning

confidence: 99%

Big Data, Internet of Things and Cloud Convergence for E-Health Applications

Suciu

Halunga

2015

New Contributions in Information Systems and Technologies

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Abstract. Big data storage and processing are considered as one of the main applications for cloud computing systems. Furthermore, the development of the Internet of Things (IoT) paradigm has advanced the research on Machine to Machine (M2M) communications and enabled novel tele-monitoring architectures for E-Health applications. However, there is a need for converging current decentralized cloud systems, general software for processing big data and IoT systems. The purpose of this paper is to analyze existing components and methods of integrating big data processing with cloud M2M systems based on Remote Telemetry Units (RTUs) and to propose a converged E-Health architecture built on Exalead CloudView, a search based application. Finally, we discuss the main findings of the proposed implementation and future directions.

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“…In addition to established markets in media on demand entertainment, advertising and social media content sharing that are currently generate and process such big media, emerging demand for management, analysis and distribution of big media is also reported in the domains of healthcare, aged-care, security, and education (just to name a few). For example, in healthcare [5] when patients return home after a surgery or a treatment will receive follow up advice and education via live as well as archived video. This may include video instructions about how to change the dressing on a healing wound, how to brush their teeth after having braces installed, or how to exercise to improve mobility.…”

mentioning

confidence: 99%