Data accuracy aware mobile healthcare applications

Amor, Lamia Ben; Lahyani, Imene; Jmaïel, Mohamed

doi:10.1016/j.compind.2018.01.020

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…Predictive Analytics In such an application scenario, the accuracy of the data is key, as it affects the health of patients. Thus, the data must be refined assiduously in order to achieve the highest possible accuracy and eliminate measurement errors [57]. However, such services also raise privacy concerns, since in addition to the health data, which intrinsically contain a vast amount of sensitive information, a vast amount of knowledge about, e.g., lifestyle, environment, and social life is disclosed [58].…”

Section: Descriptive Analyticsmentioning

confidence: 99%

Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

et al. 2022

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The present information age is characterized by an ever-increasing digitalization. Smart devices quantify our entire lives. These collected data provide the foundation for data-driven services called smart services. They are able to adapt to a given context and thus tailor their functionalities to the user’s needs. It is therefore not surprising that their main resource, namely data, is nowadays a valuable commodity that can also be traded. However, this trend does not only have positive sides, as the gathered data reveal a lot of information about various data subjects. To prevent uncontrolled insights into private or confidential matters, data protection laws restrict the processing of sensitive data. One key factor in this regard is user-friendly privacy mechanisms. In this paper, we therefore assess current state-of-the-art privacy mechanisms. To this end, we initially identify forms of data processing applied by smart services. We then discuss privacy mechanisms suited for these use cases. Our findings reveal that current state-of-the-art privacy mechanisms provide good protection in principle, but there is no compelling one-size-fits-all privacy approach. This leads to further questions regarding the practicality of these mechanisms, which we present in the form of seven thought-provoking propositions.

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Section: Descriptive Analyticsmentioning

confidence: 99%

Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

et al. 2022

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“…In most of the literature, the data quality accuracy metric has been widely focused [34]. Such as in the article [35], authors analyze the multivariate anomalies with principal component analysis and compute the accuracy of the healthcare dataset that contain the vital sign of patients who were admitted in the intensive care unit. To predict the mortality and length of staying in the hospital various benchmarking deep learning models have been applied and evaluated based on the model accuracy [36].…”

Section: Background and Related Workmentioning

confidence: 99%

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Jarwar

Chong²

2020

Applied Sciences

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Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

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“…In our previous work (Ben Amor, Lahyani, & Jmaiel, ), we have provided a detailed comparative study between various PCA algorithms. Our experimental results showed the benefits of using ROBPCA (Hubert, Rousseeuw, & Branden, ).…”

Section: Proposed Approachmentioning

confidence: 99%

“…According to our previous study (Ben Amor et al, ), we propose the use of the candid covariance‐free incremental PCA (Weng, Zhang, & Hwang, ) method to update the eigenspace model with minimum time and space complexities in the context of a resource constrained device. More details about the following step can be found in Ben Amor et al ().…”

Section: Proposed Approachmentioning

confidence: 99%

AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems

2019

Self Cite

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Mobile health care systems highly depend on collected physiological data through medical sensors to provide high‐quality care services. However, inaccurate physiological data from sensors pose a major challenge for health care providers when making decisions, whereas an erroneous decision can affect the user's life. We propose, in this paper, an anomalous data detection and isolation approach for mobile health care systems. Our approach, called AUDIT, detects inaccurate measurements in real time and distinguishes between faults or errors and health events. To do so, we propose reduced time and space complexities algorithms based on dimension reduction within the context of resource constraints. Furthermore, a decision algorithm is proposed while exploring the spatio‐temporal correlation between physiological attributes. First, we describe our approach. Then, we give its implementation details. Finally, to demonstrate the effectiveness of our approach, we show different experiments related to its detection performances and its time and space complexities.

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Data accuracy aware mobile healthcare applications

Cited by 12 publications

References 26 publications

Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

AUDIT: AnomaloUs data Detection and Isolation approach for mobile healThcare systems

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