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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 cancer during this pandemic. Lung and breast cancer are most prominently discussed, and the most concerns were expressed regarding delayed diagnosis, cancellations, missed treatments, and weakened immunity. All patients expressed significant negative sentiment, with fear being the predominant emotion. Even as some lockdown measures ease, it is crucial that patients with cancer are engaged using social media platforms for real-time identification of issues and the provision of informational and emotional support.

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 joining pre-treatment had significantly lower negative emotions after 12-months compared to post-treatment (p<0.05). Long-term deep emotion analysis reveals that all groups except ‘recurrence’ improved in emotional wellbeing. Conclusion This is the first empirical study of psychological morbidity and deep emotions expressed by men with a new diagnosis of cancer, using AI. PCa patients joining pre-treatment had improved emotions, and long-term participation in OCSG led to an

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 rehabilitat

BACKGROUND: A 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 FINDINGS: We 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

The traditional approach to health care is being revolutionized by the rapid adoption of patient‐centered healthcare models. The successful transformation of patients from passive recipients to active participants is largely attributed to increased access to healthcare information. Online support groups present a platform to seek and exchange information in an inclusive environment. As the volume of text on online support groups continues to grow exponentially, it is imperative to improve the quality of retrieved information in terms of relevance, reliability, and usefulness. We present a text‐mining approach that generates a knowledge extraction layer to address this void in personalized information retrieval from online support groups. The knowledge extraction layer encapsulates an ensemble of text‐mining techniques with a domain ontology to interpose an investigable and extensible structure on hitherto unstructured text. This structure is not limited to personalized information retrieval for patients, as it also imparts aggregates for crowdsourcing analytics by healthcare researchers. The proposed approach was successfully trialed on an active online support group consisting of 800,000 posts by 72,066 participants. Demonstrations for both patient and researcher use cases accentuate the value of the proposed approach to unlock a broad spectrum of personalized and aggregate knowledge concealed within crowdsourced content.

Topic modelling is a popular technique in text mining. However, discovered topic models are difficult to interpret due to incoherence and lack of background context. Many applications require an accurate interpretation of topic models so that both users and machines can use them effectively. Taking the advantage of random set and a domain ontology, this research can interpret the topic models. The interpretation is evaluated by comparing it with different baseline models on two standard datasets. The results show that the performance of the interpretation is significantly better than baseline models.

Selecting features from documents that describe user information needs is challenging due to the nature of text, where redundancy, synonymy, polysemy, noise and high dimensionality are common problems. The assumption that clustered documents describe only one topic can be too simple knowing that most long documents discuss multiple topics. LDA-based models show significant improvement over the cluster-based in information retrieval (IR). However, the integration of both techniques for feature selection (FS) is still limited. In this paper, we propose an innovative and effective cluster- and LDA-based model for relevance FS. The model also integrates a new extended random set theory to generalise the LDA local weights for document terms. It can assign a more discriminative weight to terms based on their appearance in LDA topics and the clustered documents. The experimental results, based on the RCV1 dataset and TREC topics for information filtering (IF), show that our model significantly outperforms eight state-of-the-art baseline models in five standard performance measures.