BACKGROUND: Stroke is a major worldwide cause of serious long-term disability. Most previous studies addressing functional independence included only inpatients with limited follow-up. OBJECTIVE: To identify novel classes of patients having similar temporal patterns in motor functional independence and relate them to baseline clinical features. METHODS: Retrospective observational cohort study, data were obtained for n = 428 adult patients with ischemic stroke admitted to rehabilitation (March 2005–March 2020), including baseline clinical features and follow-ups of motor Functional Independence Measure (mFIM) categorized as poor, fair or good. Growth mixture models (GMMs) were fitted to identify classes of patients with similar mFIM trajectories. RESULTS: GMM identified three classes of trajectories (1,664 mFIM assessments): C1 (11.2 %), 97.9% having poor admission mFIM, at 4.93 years 61.1% still poor, with the largest percentage of hypertension, neglect, dysphagia, diabetes and dyslipidemia of all three classes. C2 (23.1%), 99% had poor admission mFIM, 25% poor discharge mFIM, the largest percentage of aphasia and greatest mFIM gain, at 4.93 years only 6.2% still poor. C3 (65.7%) the youngest, lowest NIHSS, 37.7% poor admission mFIM, 73% good discharge mFIM, only 4.6% poor discharge mFIM, 90% good at 4.93 years. CONCLUSIONS: GMM identified novel motor functional classes characterized by baseline features.

Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, “You shall know a word by the company it keeps,” highlighting the importance of context in NLP. Meanwhile, “Context is everything in Emotion Research.” Therefore, we aimed to train a model (W2V) for generating word associations (also known as embeddings) using a popular Coronavirus Reddit forum, validate them using public evidence and apply them to the discovery of context for specific emotions previously reported as related to psychological resilience. We used Pushshiftr, quanteda, broom, wordVectors, and superheat R packages. We collected all 374,421 posts submitted by 104,351 users to Reddit/Coronavirus forum between January 2020 and July 2021. W2V identified 64 terms representing the context for seven positive emotions (gratitude, compassion, love, relief, hope, calm, and admiration) and 52 terms for seven negative emotions (anger, loneliness, boredom, fear, anxiety, confusion, sadness) all from valid experienced situations. We clustered them visually, highlighting contextual similarity. Although trained on a “small” dataset, W2V can be used for context discovery to expand on concepts such as psychological resilience.

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The quality of life of post-ischemic stroke patients during reintegration is affected by a range of factors, including the risk of insufficient social and family support, as well as socio-economic status. A patient who is unable to access needed social support, such as home health care or a day center, is at a greater risk of poorer quality of life during reintegration.Consequently, the key goals of post-stroke reintegration are to improve patient outcomes across these factors, to inform reintegration decisions, as well as design personalized interventions for patients with social risk. This chapter presents a case-study of 240 patients of the Catalonia region of Spain that uses data visualization techniques (known as Sankey diagrams) to provide insight into changes in quality of life risk factors such as gender, and stroke severity, during reintegration. As supported by the case-study, social risk is a complex and multifactorial phenomenon that can vary significantly for an individual over the course of stroke rehabilitation and reintegration.

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