Remote health monitoring adoption model based on artificial neural networks

Huang, Jui-Chen

doi:10.1016/j.eswa.2009.05.063

Cited by 44 publications

(26 citation statements)

References 37 publications

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“…The results indicated that it is feasible to construct a model with ANN, and identify telecare adoption model by using ANN based on the healthcare information adoption model (HIAM) that is created first time by Huang (2010). The finding may offers significant reference for subsequent studies.…”

Section: Discussionmentioning

confidence: 92%

Telecare Adoption Model Based on Artificial Neural Networks

Huang¹

2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

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Section: Discussionmentioning

confidence: 92%

Telecare Adoption Model Based on Artificial Neural Networks

Huang¹

2011

Artificial Neural Networks - Methodological Advances and Biomedical Applications

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“…Recent papers suggested the use of ANNs in evaluating implementation model of remote health observing based on suitable questionnaire (Huang, 2010) and implementing web-based health monitoring systems for spread use by patients in their home environment (Youm, 2011).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

The Use of Artificial Neural Networks in Clinical Medicine

Jovanović¹,

Milenković²,

Perković³

et al. 2016

Proceedings of the International Scientific Conference - Sinteza 2016

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Digital Agenda in Serbia involves the introduction of an electronic system for monitoring of the main characteristics of patients, disease progression and treatment outcomes through EHR (Electronic Health Record). Internationally standardized data set contains more than 150 variables, with a tendency to introduce new frequently. In addition to the increased demand for treatment, there are also demands for optimizing the health care system. In order to predict the likelihood of diagnosis, course and outcome of treatment, classically multivariate regression linear logistic model is being used. In recent years, studies indicate that the use of Artificial Neural Networks (ANN) may provide improved results in terms of likelihood of final diagnosis and outcomes that include input variables which, by their nature, have a non-linear interdependence. We reviewed current ANN models, their advantages and disadvantages compared to common regression models and their applicability in clinical practice. Also, we analyzed and suggested models that could possibly optimize the process of diagnosis, predict the cost and duration of treatment and rationalize medical and other resources by reducing the cost/ benefit coefficient per patient.

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“…PTTB: is the degree of willingness to share knowledge or the degree of trust to which an individual will not intentionally take advantage of a certain situation. PTBS: are the biases people have in trust toward knowledge sharing, (Rosenstock 1966(Rosenstock , 1974Huang, 2010). ECTT: are the external factors that affect trust and knowledge sharing (Huang, 2010;Strecher & Rosenstock, 1997).…”

Section: Trust-based Knowledge Sharing Adoption Model (Tbksam)mentioning

confidence: 99%

Knowledge Sharing Trust Level Measurement Adoption Model Based On Fuzzy Expert System

Folorunso¹

2015

CIS

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In this paper, a fuzzy expert system-based Knowledge Sharing Trust Level(KSTL) measurement adoption model is presented. The KSTL was modeled using four input variables, developed from Technology Acceptance Model constructs namely; Perceived Trust Towards Competence, Perceived Trust Towards Benevolence, Perceived Trust Barrier for Sharing, External Cue Towards Trust to determine KSTL. A KSTL-fuzzy algorithm was developed using a trust metric equation at the preprocessing stage and implemented using Matlab 7.6.0 to compute KSTL crisp_value. The results obtained provided a useful understanding about the degree of trust among Community of Practice practicing knowledge-sharing. The proposed work was found to be dynamic, as the computed KSTL fluctuates with changes in the input variables. The simulated results demonstrate the effectiveness of the model in measuring trust level in knowledge-sharing applications.

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Remote health monitoring adoption model based on artificial neural networks

Cited by 44 publications

References 37 publications

Telecare Adoption Model Based on Artificial Neural Networks

Telecare Adoption Model Based on Artificial Neural Networks

The Use of Artificial Neural Networks in Clinical Medicine

Knowledge Sharing Trust Level Measurement Adoption Model Based On Fuzzy Expert System

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