Emotional Awareness and Decision-Making in the Context of Computer-Mediated Psychotherapy

Alvandi, Ebrahim Oshni; Doorn, George Van; Symmons, Mark

doi:10.1007/s41666-019-00050-7

Cited by 7 publications

(4 citation statements)

References 73 publications

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“…This section includes a survey of related research on the application of computer technology, artificial intelligence, and machine learning algorithms for diagnosing, monitoring, and assessing psychological states and emotional behaviour. Computer-mediated communication and its effect on emotional awareness were investigated in [25]. Patients in computer-mediated psychotherapy were shown to be able to demonstrate emotional awareness.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent-Based System for Detecting Depression on Social Media Platform

Ojo,

Adewuyi,

Oni

et al. 2023

Preprint

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Purpose: The global quarantine measures implemented in response to the coronavirus outbreak have resulted in a surge of psychiatric issues, including but not limited to depressive symptoms, anxiety, and sleep disturbances, which have placed a significant burden on healthcare systems worldwide. The most efficacious prevention strategy for depression is the identification of its early warning symptoms. In recent times, the healthcare sector has increasingly embraced a technology-oriented strategy. The examination of social media content possesses the capability to significantly reduce the incidence of depression and, consequently, the number of suicides. Methods: The present study introduces a computational method to detect social media users who exhibit symptoms of depression. The intelligent framework employs machine learning techniques to identify text in social media posts that pertain to depression. In this study, three distinct machine learning models, namely the bidirectional encoder representations from Transformers (BERT), the long short-term memory (LSTM), and the convolutional neural network (CNN) were subjected to 1 Springer Nature 2021 L A T E X template Article Title pre-processing and pre-training using data sets obtained from the Twitter API and the Reddit database. The ultimate goal of this study is to accurately distinguish between depressive and non-depressive content. Results: According to the empirical analysis, the BERT model demonstrated a 92.4% accuracy rate and achieved the most optimal validation loss during the third epoch. Moreover, in comparison to the CNN and LSTM models, the BERT model exhibited greater efficiency across all performance indicators. Conclusion: The BERT model performed exceptionally well in both text categorization and feature extraction when compared to the other pre-training models utilized in this investigation. The BERT model has been refined by the identification of these unique characteristics, making it better suited to the task of identifying depressive-related information in digital documents and social media posts.

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Section: Related Workmentioning

confidence: 99%

An Intelligent-Based System for Detecting Depression on Social Media Platform

Ojo,

Adewuyi,

Oni

et al. 2023

Preprint

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“…For an in-depth GUI evaluation, the influence of emotions must be also considered. The authors of [100] considered the communication during tele-mental health psychotherapy sessions between a physician and a patient. In particular, this study showed that the emotions are involved in the decisional process, even when the physician-patient relationship is mediated via a computer, suggesting that emotional awareness is a key cognitive factor in remote diagnosis and therapy.…”

Section: Usability and Performance Evaluationmentioning

confidence: 99%

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine

Rundo

Pirrone

Vitabile

et al. 2020

Journal of Biomedical Informatics

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The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes,

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“…Recently, considerable research has been devoted to investigate technical and human issues which motivate the development and adoption of information technology (IT) in health services as part of progressing with a DH ecosystem. Specifically, numerous studies have explored the adoption of IT technologies by different user types, as well as the impact of IT interventions on health outcomes for individuals [ 5 – 9 ]. In their field study, Mercer et al [ 10 ] demonstrated that older adults living with chronic illness would adopt wearable activity-tracking technologies as long as such technologies are perceived useful in promoting their self-awareness, improving their wellbeing and motivating physical activity.…”

Section: Introductionmentioning

confidence: 99%

Understanding digital health ecosystem from Australian citizens’ perspective: A scoping review

2021

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Background Digital health (DH) and the benefits of related services are fairly well understood. However, it still is critical to map the digital health care landscape including the key elements that define it as an ecosystem. Particularly, knowing the perspectives of citizens on this digital transformation is an important angle to capture. In this review we aim to analyze the relevant studies to identify how DH is understood and experienced by Australian citizens and what they may require from DH platforms. Materials and methods A scoping literature review was conducted across several electronic databases (ACM Digital Library, OVID, PubMed, Scopus, IEEE, Science Direct, SAGE), as well as grey literature. Additionally, citation mining was conducted to identify further relevant studies. Identified studies were subjected to eligibility criteria and the final set of articles was independently reviewed, analyzed, discussed and interpreted by three reviewers. Results Of 3811 articles, 98 articles met the inclusion criteria with research-based articles–as opposed to review articles or white papers– comprising the largest proportion (72%) of the selected literature. The qualitative analysis of the literature revealed five key elements that capture the essence of the digital health ecosystem interventions from the viewpoint of the Australian citizens. The identified elements were “consumer/user”, “health care”, “technology”, “use and usability”, “data and information”. These elements were further found to be associated with 127 subcategories. Conclusions This study is the first of its kind to analyze and synthesize the relevant literature on DH ecosystems from the citizens’ perspective. Through the lens of two research questions, this study defines the key components that were found crucial to understanding citizens’ experiences with DH. This understanding lays a strong foundation for designing and fostering DH ecosystem. The results provide a solid ground for empirical testing.

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Emotional Awareness and Decision-Making in the Context of Computer-Mediated Psychotherapy

Cited by 7 publications

References 73 publications

An Intelligent-Based System for Detecting Depression on Social Media Platform

An Intelligent-Based System for Detecting Depression on Social Media Platform

Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine

Understanding digital health ecosystem from Australian citizens’ perspective: A scoping review

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