Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy

2021 ◽  
pp. 1-33
Author(s):  
Sebastian Laacke ◽  
Regina Mueller ◽  
Georg Schomerus ◽  
Sabine Salloch
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Concept Of Health ◽  
Health Related

Artificial Intelligence, Social Media and Depression. ‘Patient’ Autonomy Revisited

2021 ◽  
Vol 66 (Special Issue) ◽  
pp. 133-133
Author(s):  
Regina Mueller ◽  
◽  
Sebastian Laacke ◽  
Georg Schomerus ◽  
Sabine Salloch ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Mental Disorders ◽  
Patient Autonomy ◽  
Ethical Challenges ◽  
Traditional Concept ◽  
Ethical Implications ◽  
Health Related ◽  
Twitter Users ◽  
Using Data

"Artificial Intelligence (AI) systems are increasingly being developed and various applications are already used in medical practice. This development promises improvements in prediction, diagnostics and treatment decisions. As one example, in the field of psychiatry, AI systems can already successfully detect markers of mental disorders such as depression. By using data from social media (e.g. Instagram or Twitter), users who are at risk of mental disorders can be identified. This potential of AI-based depression detectors (AIDD) opens chances, such as quick and inexpensive diagnoses, but also leads to ethical challenges especially regarding users’ autonomy. The focus of the presentation is on autonomy-related ethical implications of AI systems using social media data to identify users with a high risk of suffering from depression. First, technical examples and potential usage scenarios of AIDD are introduced. Second, it is demonstrated that the traditional concept of patient autonomy according to Beauchamp and Childress does not fully account for the ethical implications associated with AIDD. Third, an extended concept of “Health-Related Digital Autonomy” (HRDA) is presented. Conceptual aspects and normative criteria of HRDA are discussed. As a result, HRDA covers the elusive area between social media users and patients. "

Trend of Parental Refusal or Delay of Childhood Vaccinations

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Meagan Marie Daoust
Keyword(s):  
Public Health ◽  
Social Media ◽  
Healthcare System ◽  
Educational Context ◽  
Health Consequences ◽  
Missed Opportunities ◽  
The Public ◽  
Health Related ◽  
Parental Refusal

The healthcare trend of parental refusal or delay of childhood vaccinations will be investigated through a complex Cynefin Framework component in an economic and educational context, allowing patterns to emerge that suggest recommendations of change for the RN role and healthcare system. As a major contributing factor adding complexity to this trend, social media is heavily used for health related knowledge, making it is difficult to determine which information is most trustworthy. Missed opportunities for immunization can result, leading to economic and health consequences for the healthcare system and population. Through analysis of the powerful impact social media has on this evolving trend and public health, an upstream recommendation for RNs to respond with is to utilize reliable social media to the parents’ advantage within practice. The healthcare system should focus on incorporating vaccine-related education into existing programs and classes offered to parents, and implementing new vaccine classes for the public.

Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases (Preprint)

2018 ◽  
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
Jose Crespo
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Related ◽  
Health Social Network ◽  
Discussion System ◽  
Clinical Cases

BACKGROUND Internet and Social media is an enormous source of information. Health Social Networks and online collaborative environments enable users to create shared content that afterwards can be discussed. While social media discussions for health related matters constitute a potential source of knowledge, characterizing the relevance of participations from different users is a challenging task. OBJECTIVE The aim of this paper is to present a methodology designed for quantifying relevant information provided by different participants in clinical online discussions. METHODS A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. These indicators make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. RESULTS Proposed indicators have been applied to two discussions extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. CONCLUSIONS The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a Health Social Network.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Using the Brief Health Literacy Screen in Chronic Care in French Hospital Settings: Content Validity of Patient and Healthcare Professional Reports

2020 ◽  
Vol 18 (1) ◽  
pp. 96
Author(s):  
Adèle Perrin ◽  
Luiza Siqueira do Prado ◽  
Amélie Duché ◽  
Anne-Marie Schott ◽  
Alexandra L. Dima ◽  
...  
Keyword(s):  
Health Literacy ◽  
Content Validity ◽  
Chronic Care ◽  
Routine Care ◽  
Related Information ◽  
Concept Of Health ◽  
Patient Reported ◽  
Centered Care ◽  
Health Related ◽  
French Hospital

Person-centered care has led healthcare professionals (HCPs) to be more attentive to patients’ ability to understand and apply health-related information, especially those with chronic conditions. The concept of health literacy (HL) is essential in understanding patients’ needs in routine care, but its measurement is still controversial, and few tools are validated in French. We therefore considered the brief health literacy screen (BHLS) for assessing patient-reported HL in chronic care settings, and also developed an HCP-reported version of the BHLS with the aim of using it as a research instrument to assess HCPs’ evaluation of patients’ HL levels. We assessed the content validity of the French translation of both the patient-reported and HCP-reported BHLS in chronic care within hospital settings, through cognitive interviews with patients and HCPs. We performed qualitative analysis on interview data using the survey response Tourangeau model. Our results show that the BHLS is easy and quick to administer, but some terms need to be adapted to the French chronic care settings. Health-related information was observed to be mainly communicated orally, hence a useful direction for future literacy measures would be to also address verbal HL.

scholarly journals Rapid deployment of a community engagement study and educational trial via social media: implementation of the UC-COVID study

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lauren E. Wisk ◽  
Russell G. Buhr
Keyword(s):  
Social Media ◽  
Search Activity ◽  
Community Partners ◽  
Community Values ◽  
Sample Representativeness ◽  
Adult Participants ◽  
Health Related ◽  
Social Media Platforms ◽  
Rapid Deployment

Abstract Background In response to the COVID-19 pandemic and associated adoption of scarce resource allocation (SRA) policies, we sought to rapidly deploy a novel survey to ascertain community values and preferences for SRA and to test the utility of a brief intervention to improve knowledge of and values alignment with a new SRA policy. Given social distancing and precipitous evolution of the pandemic, Internet-enabled recruitment was deemed the best method to engage a community-based sample. We quantify the efficiency and acceptability of this Internet-based recruitment for engaging a trial cohort and describe the approach used for implementing a health-related trial entirely online using off-the-shelf tools. Methods We recruited 1971 adult participants (≥ 18 years) via engagement with community partners and organizations and outreach through direct and social media messaging. We quantified response rate and participant characteristics of our sample, examine sample representativeness, and evaluate potential non-response bias. Results Recruitment was similarly derived from direct referral from partner organizations and broader social media based outreach, with extremely low study entry from organic (non-invited) search activity. Of social media platforms, Facebook was the highest yield recruitment source. Bot activity was present but minimal and identifiable through meta-data and engagement behavior. Recruited participants differed from broader populations in terms of sex, ethnicity, and education, but had similar prevalence of chronic conditions. Retention was satisfactory, with entrance into the first follow-up survey for 61% of those invited. Conclusions We demonstrate that rapid recruitment into a longitudinal intervention trial via social media is feasible, efficient, and acceptable. Recruitment in conjunction with community partners representing target populations, and with outreach across multiple platforms, is recommended to optimize sample size and diversity. Trial implementation, engagement tracking, and retention are feasible with off-the-shelf tools using preexisting platforms. Trial registration ClinicalTrials.gov NCT04373135. Registered on May 4, 2020

scholarly journals Artificial Intelligence, Social Media, and Suicide Prevention: Principle of Beneficence Besides Respect for Autonomy

2021 ◽  
Vol 21 (7) ◽  
pp. 43-45
Author(s):  
Hui Zhang ◽  
Yuming Wang ◽  
Zhenxiang Zhang ◽  
Fangxia Guan ◽  
Hongmei Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Suicide Prevention ◽  
Respect For Autonomy

scholarly journals Studying the COVID-19 infodemic at scale

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110211
Author(s):  
Anatoliy Gruzd ◽  
Manlio De Domenico ◽  
Pier Luigi Sacco ◽  
Sylvie Briand
Keyword(s):  
Social Media ◽  
Big Data ◽  
Interdisciplinary Research ◽  
Leading Edge ◽  
Theme Issue ◽  
Misleading Information ◽  
Effective Strategies ◽  
Health Related

This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related (mis-)information is circulating on social media. In particular, we are focusing on how computational and Big Data approaches can help to provide a better understanding of the ongoing COVID-19 infodemic (overexposure to both accurate and misleading information on a health topic) and to develop effective strategies to combat it.

Disinformation is a rising security threat to the West

2021 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
The United States ◽  
Step Change ◽  
Media Discourse ◽  
Security Threat ◽  
The West ◽  
Content Type ◽  
Synthetic Media ◽  
Victims Of Bullying

Significance Articles containing the bogus quotes were shared across social media globally. The case illustrates how disinformation is created and spread for malign influence, and its ease of entry into social media discourse, which makes it so difficult to untangle and counter. Impacts Political polarisation within the United States is impeding a 'whole of society' response. Russian and Chinese disinformation campaigns will claim the two nations are falsely accused victims of bullying by envious foes. Artificial intelligence-created synthetic media such as deepfakes will enable a step-change in the sophistication of 'infowars'.

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