Understanding how social media imagery empowers caregivers: an analysis of microcephaly in Latin America

Motti, Vivian Genaro; Kalantari, Niloofar; Neris, Vânia Paula de Almeida

doi:10.1007/s00779-020-01418-y

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…The risks involved are even higher when vulnerable populations are at stake (see Chap. 15), for instance, with parents sharing information about children [18] or caregivers sharing information about patients with disabilities.…”

Section: Sources and Stakeholdersmentioning

confidence: 99%

Healthcare Privacy

Motti

Berkovsky

2022

Modern Socio-Technical Perspectives on Privacy

Self Cite

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As healthcare shifts towards the digital realm and healthcare delivery steers to patient-centric solutions, new privacy risks emerge. Such risks are acknowledged, but understanding and addressing them with privacy-enhanced technologies is practically challenging. This chapter describes privacy concerns and risks that emerge with the digitization of healthcare services, the availability of Internet-of-care-things, and the usage of online services for medical data. To ensure patients’ privacy, collaborative efforts from stakeholders are necessary. Patients, practitioners, and family members play an important role, along with medical organizations, including hospitals, insurance companies, and clinics. Privacy-preserving mechanisms go beyond the protection of patients’ data to the infrastructure of medical devices, networks, and systems. The data life cycle, from collection to disposal, must be considered when implementing privacy protections. Principles, policies, and regulations addressing privacy are limited and costly to implement, failing to cover novel technologies that collect and transmit medical data. In the USA, HIPAA is the de facto policy standard. Nevertheless, HIPAA disregards data collected by wearable sensors, fitness trackers, and smartwatches. It does not consider social media networks, mobile applications, and discussion forums where users share medical information. Lastly, genetic data available through online profiles rises privacy issues that are neither known nor regulated.

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Section: Sources and Stakeholdersmentioning

confidence: 99%

Healthcare Privacy

Motti

Berkovsky

2022

Modern Socio-Technical Perspectives on Privacy

Self Cite

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“…The fourth article presents the work of Motti et al [ 12 ], and focuses on an analysis of Instagram imagery related to microcephaly in Spanish and Portuguese language. In particular, the analysis focuses on users’ behaviors when using Instagram to communicate about microcephaly.…”

Section: Submission and Summary Of Contributionsmentioning

confidence: 99%

Human computer-interaction in Latin America

Castro

Gaytán-Lugo

Santana-Mancilla

et al. 2021

Pers Ubiquit Comput

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“…Natural language processing (NLP) leverages representation learning approaches across various domains, including text classification [32], question answering [33], machine translation [34]- [36], electronic health records [37], financial forecasting [38], chatbots [39], social media analysis [40], [41] and more. The field of NLP has witnessed an evolution from early rule-based methods to the application of statistical learning techniques, enabled by access to large amounts of data.…”

Section: Introductionmentioning

confidence: 99%

Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges

Payandeh,

Baghaei,

Fayyazsanavi

et al. 2023

IEEE Access

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Machine learning algorithms have had a profound impact on the field of computer science over the past few decades. The performance of these algorithms heavily depends on the representations derived from the data during the learning process. Successful learning processes aim to produce concise, discrete, meaningful representations that can be effectively applied to various tasks. Recent advancements in deep learning models have proven to be highly effective in capturing high-dimensional, non-linear, and multi-modal characteristics. In this work, we provide a comprehensive overview of the current state-of-the-art in deep representation learning and the principles and developments made in the process of representation learning. Our study encompasses both supervised and unsupervised methods, including popular techniques such as autoencoders, self-supervised methods, and deep neural networks. Furthermore, we explore a wide range of applications, including image recognition and natural language processing. In addition, we discuss recent trends, key issues, and open challenges in the field. This survey endeavors to make a significant contribution to the field of deep representation learning, fostering its understanding and facilitating further advancements.

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Understanding how social media imagery empowers caregivers: an analysis of microcephaly in Latin America

Cited by 7 publications

References 36 publications

Healthcare Privacy

Healthcare Privacy

Human computer-interaction in Latin America

Deep Representation Learning: Fundamentals, Technologies, Applications, and Open Challenges

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