The lockdown measures of the ongoing COVID-19 pandemic have disengaged patients with cancer from formal health care settings, leading to an increased use of social media platforms to address unmet needs and expectations. Although remote health technologies have addressed some of the medical needs, the emotional and mental well-being of these patients remain underexplored and underreported. We used a validated artificial intelligence framework to conduct a comprehensive real-time analysis of two data sets of 2,469,822 tweets and 21,800 discussions by patients with

The technological landscape of intelligent transport systems (ITS) has been radically transformed by the emergence of the big data streams generated by the Internet of Things (IoT), smart sensors, surveillance feeds, social media, as well as growing infrastructure needs. It is timely and pertinent that ITS harness the potential of an artificial intelligence (AI) to develop the big data-driven smart traffic management solutions for effective decision-making. The existing AI techniques that function in isolation exhibit clear limitations in developing a comprehensive platform due to the dynamicity of big data streams, highfrequency unlabeled data generation from the heterogeneous data sources, and volatility of traffic conditions. In this paper, we propose an expansive smart traffic management platform (STMP) based on the unsupervised online incremental machine learning, deep learning, and deep reinforcement learning to address these limitations. The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and impact propagation, traffic flow forecasting, commuter sentiment analysis, and optimized traffic control decisions. The platform is successfully demonstrated on 190 million records of smart sensor network traffic data generated by 545,851 commuters and corresponding social media data on the arterial road network of Victoria, Australia.

Background: Compared with adults in the general population, autistic adults are more likely to experience poor mental health, which can contribute to increased suicidality. While the autistic community has long identified autistic burnout as a significant mental health risk, to date, only one study has been published. Early research has highlighted the harmful impact of autistic burnout among autistic adults and the urgent need to better understand this phenomenon. Methods: To understand the lived experiences of autistic adults, we used data scraping to extract public posts about autistic burnout from 2 online platforms shared between 2005 and 2019, which yielded 1127 posts. Using reflexive thematic analysis and an inductive ''bottom-up'' approach, we sought to understand the etiology, symptoms, and impact of autistic burnout, as well as prevention and recovery strategies. Two autistic researchers with self-reported experience of autistic burnout reviewed the themes and provided insight and feedback. Results: We identified eight primary themes and three subthemes across the data. (1) Systemic, pervasive lack of autism awareness. (1.1) Discrimination and stigma. (2) A chronic or recurrent condition. (3) Direct impact on health and well-being. (4) A life unlived. (5) A blessing in disguise? (6) Self-awareness and personal control influence risk. (6.1) ''You need enough balloons to manage the weight of the rocks.'' (7) Masking: Damned if you do, damned if you don't. (8) Ask the experts. (8.1) Stronger together. The overarching theme was that a pervasive lack of awareness and stigma about autism underlie autistic burnout. Conclusions: We identified a set of distinct yet interrelated factors that characterize autistic burnout as a recurring condition that can, directly and indirectly, impact autistic people's functioning, mental health, quality of life, and well-being. The findings suggest that increased awareness and acceptance of autism could be key to burnout prevention and recovery.

BackgroundA primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines.Methods and findingsWe have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed.ConclusionsFindings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.

No abstract

Background:The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic.Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework.Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results:The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles.Conclusions: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic...

Aim. Neural plastic changes are experience and learning dependent, yet exploiting this knowledge to enhance clinical outcomes after stroke is in its infancy. Our aim was to search the available evidence for the core concepts of neuroplasticity, stroke recovery, and learning; identify links between these concepts; and identify and review the themes that best characterise the intersection of these three concepts. Methods. We developed a novel approach to identify the common research topics among the three areas: neuroplasticity, stroke recovery, and learning. A concept map was created a priori, and separate searches were conducted for each concept. The methodology involved three main phases: data collection and filtering, development of a clinical vocabulary, and the development of an automatic clinical text processing engine to aid the process and identify the unique and common topics. The common themes from the intersection of the three concepts were identified. These were then reviewed, with particular reference to the top 30 articles identified as intersecting these concepts. Results. The search of the three concepts separately yielded 405,636 publications. Publications were filtered to include only human studies, generating 263,751 publications related to the concepts of neuroplasticity (n=6,498), stroke recovery (n=79,060), and learning (n=178,193). A cluster concept map (network graph) was generated from the results; indicating the concept nodes, strength of link between nodes, and the intersection between all three concepts. We identified 23 common themes (topics) and the top 30 articles that best represent the intersecting themes. A time-linked pattern emerged. Discussion and Conclusions. Our novel approach developed for this review allowed the identification of the common themes/topics that intersect the concepts of neuroplasticity, stroke recovery, and learning. These may be synthesised to advance a neuroscience-informed approach to stroke rehabilitation. We also identified gaps in available literature using this approach. These may help guide future targeted research.

Background Online Cancer Support Groups (OCSG) are becoming an increasingly vital source of information, experiences and empowerment for patients with cancer. Despite significant contributions to physical, psychological and emotional wellbeing of patients, OCSG are yet to be formally recognised and used in multidisciplinary cancer support programs. This study highlights the opportunity of using Artificial Intelligence (AI) in OCSG to address psychological morbidity, with supporting empirical evidence from prostate cancer (PCa) patients. Methods A validated framework of AI techniques and Natural Language Processing (NLP) methods, was used to investigate PCa patient activities based on conversations in ten international OCSG (18,496 patients-277,805 conversations). The specific focus was on activities that indicate psychological morbidity; the reasons for joining OCSG, deep emotions and the variation from joining through to milestones in the cancer trajectory. Comparative analyses were conducted using t-tests, One-way ANOVA and Tukey-Kramer post-hoc analysis. Findings PCa patients joined OCSG at four key phases of psychological distress; diagnosis, treatment, side-effects, and recurrence, the majority group was 'treatment' (61.72%). The four groups varied in expression of the intense emotional burden of cancer. The 'side-effects' group expressed increased negative emotions during the first month compared to other groups (p<0.01). A comparison of pre-treatment vs post-treatment emotions showed that