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.

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