Discovering the Context of People With Disabilities : Semantic Categorization Test and Environmental Factors Mapping of Word Embeddings from Reddit

The World Health Organization's International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing "context" as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them. The objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities. Reddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde's updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation. We analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast - 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (≤ 2 seconds) to the terms related to ADLs, ranging from apps "to know accessibility before you go" to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was "resilience," recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the "resilience" definition. This study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy

Accurate early predictions of a patient\u27s likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied)

Cognitive prognosis of acquired brain injury patients using machine learning techniques

The cognitive prognosis of acquired brain injury (ABI) patients is a valuable tool for an improved and personalized treatment. In this paper, we explore the task of automatic cognitive prognosis of ABI patients via machine learning techniques. Based on a set of pre-treatment assessments, distinct classifiers are trained to predict whether the patient will improve in one or any of three cognitive areas: attention, memory, and executive functioning. Results show that variables such as the age at the moment of the injury, the patient's etiology, or the neuropsychological evaluation scores obtained before the treatment are relevant for prognosis and easily yield statistically significant accuracies. Additionally, the prognostic relevance of these and other variables is studied by means of standard feature selection methodologies. The outputs of the present paper add to the discussion on current cognitive rehabilitation practices and push towards the exploitation of existing technologies for improving medical evaluations and treatments.We thank all the patients and staff from Institut Guttmann who cooperated in data collection. This work has been partially funded by TIN-2012-38450-C03-03 from the Spanish Government (all authors), JAEDOC069/2010 from Consejo Superior de Investigaciones Cientıficas (J.S.), and 2009-SGR-1434 from Generalitat de CatalunyaPeer Reviewe

Stroke Survivors on Twitter : Sentiment and Topic Analysis From a Gender Perspective

BACKGROUND: Stroke is the worldwide leading cause of long-term disabilities. Women experience more activity limitations, worse health-related quality of life, and more poststroke depression than men. Twitter is increasingly used by individuals to broadcast their day-to-day happenings, providing unobtrusive access to samples of spontaneously expressed opinions on all types of topics and emotions. OBJECTIVE: This study aimed to consider the raw frequencies of words in the collection of tweets posted by a sample of stroke survivors and to compare the posts by gender of the survivor for 8 basic emotions (anger, fear, anticipation, surprise, joy, sadness, trust and disgust); determine the proportion of each emotion in the collection of tweets and statistically compare each of them by gender of the survivor; extract the main topics (represented as sets of words) that occur in the collection of tweets, relative to each gender; and assign happiness scores to tweets and topics (using a well-established tool) and compare them by gender of the survivor. METHODS: We performed sentiment analysis based on a state-of-the-art lexicon (National Research Council) with syuzhet R package. The emotion scores for men and women were first subjected to an F-test and then to a Wilcoxon rank sum test. We extended the emotional analysis, assigning happiness scores with the hedonometer (a tool specifically designed considering Twitter inputs). We calculated daily happiness average scores for all tweets. We created a term map for an exploratory clustering analysis using VosViewer software. We performed structural topic modelling with stm R package, allowing us to identify main topics by gender. We assigned happiness scores to all the words defining the main identified topics and compared them by gender. RESULTS: We analyzed 800,424 tweets posted from August 1, 2007 to December 1, 2018, by 479 stroke survivors: Women (n=244) posted 396,898 tweets, and men (n=235) posted 403,526 tweets. The stroke survivor condition and gender as well as membership in at least 3 stroke-specific Twitter lists of active users were manually verified for all 479 participants. Their total number of tweets since 2007 was 5,257,433; therefore, we analyzed the most recent 15.2% of all their tweets. Positive emotions (anticipation, trust, and joy) were significantly higher (P<.001) in women, while negative emotions (disgust, fear, and sadness) were significantly higher (P<.001) in men in the analysis of raw frequencies and proportion of emotions. Happiness mean scores throughout the considered period show higher levels of happiness in women. We calculated the top 20 topics (with percentages and CIs) more likely addressed by gender and found that women's topics show higher levels of happiness scores. CONCLUSIONS: We applied two different approaches-the Plutchik model and hedonometer tool-to a sample of stroke survivors' tweets. We conclude that women express positive emotions and happiness much more than men

Personalized Web-Based Cognitive Rehabilitation Treatments for Patients with Traumatic Brain Injury : Cluster Analysis

Funding: This study was partially funded by the INNOBRAIN project: New Technologies for Innovation in Cognitive Stimulation and Rehabilitation (COMRDI15-1-0017). ACCIÓ-Comunitat RIS3CAT d'innovació en salut NEXTHEALTH (COM15-1-0004) cofinanced this project under the FEDER Catalonia 2014-2020 Operational ProgramTraumatic brain injury (TBI) is a leading cause of disability worldwide. TBI is a highly heterogeneous disease, which makes it complex for effective therapeutic interventions. Cluster analysis has been extensively applied in previous research studies to identify homogeneous subgroups based on performance in neuropsychological baseline tests. Nevertheless, most analyzed samples are rarely larger than a size of 100, and different cluster analysis approaches and cluster validity indices have been scarcely compared or applied in web-based rehabilitation treatments. The aims of our study were as follows: (1) to apply state-of-the-art cluster validity indices to different cluster strategies: hierarchical, partitional, and model-based, (2) to apply combined strategies of dimensionality reduction by using principal component analysis and random forests and perform stability assessment of the final profiles, (3) to characterize the identified profiles by using demographic and clinically relevant variables, and (4) to study the external validity of the obtained clusters by considering 3 relevant aspects of TBI rehabilitation: Glasgow Coma Scale, functional independence measure, and execution of web-based cognitive tasks. This study was performed from August 2008 to July 2019. Different cluster strategies were executed with Mclust, factoextra, and cluster R packages. For combined strategies, we used the FactoMineR and random forest R packages. Stability analysis was performed with the fpc R package. Between-group comparisons for external validation were performed using 2-tailed t test, chi-square test, or Mann-Whitney U test, as appropriate. We analyzed 574 adult patients with TBI (mostly severe) who were undergoing web-based rehabilitation. We identified and characterized 3 clusters with strong internal validation: (1) moderate attentional impairment and moderate dysexecutive syndrome with mild memory impairment and normal spatiotemporal perception, with almost 66% (111/170) of the patients being highly educated (P <.05); (2) severe dysexecutive syndrome with severe attentional and memory impairments and normal spatiotemporal perception, with 49.2% (153/311) of the patients being highly educated (P <.05); (3) very severe cognitive impairment, with 45.2% (42/93) of the patients being highly educated (P <.05). We externally validated them with severity of injury (P =.006) and functional independence assessments: cognitive (P <.001), motor (P <.001), and total (P <.001). We mapped 151,763 web-based cognitive rehabilitation tasks during the whole period to the 3 obtained clusters (P <.001) and confirmed the identified patterns. Stability analysis indicated that clusters 1 and 2 were respectively rated as 0.60 and 0.75; therefore, they were measuring a pattern and cluster 3 was rated as highly stable. Cluster analysis in web-based cognitive rehabilitation treatments enables the identification and characterization of strong response patterns to neuropsychological tests, external validation of the obtained clusters, tailoring of cognitive web-based tasks executed in the web platform to the identified profiles, thereby providing clinicians a tool for treatment personalization, and the extension of a similar approach to other medical conditions

Subacute stroke physical rehabilitation evidence in activities of daily living outcomes

Supplemental Digital Content is available in the text Stroke is a leading cause of disabilities worldwide. One of the key disciplines in stroke rehabilitation is physical therapy which is primarily aimed at restoring and maintaining activities of daily living (ADL). Several meta-analyses have found different interventions improving functional capacity and reducing disability. To systematically evaluate existing evidence, from published systematic reviews of meta-analyses, of subacute physical rehabilitation interventions in (ADLs) for stroke patients. Umbrella review on meta-analyses of RCTs ADLs in MEDLINE, Web of Science, Scopus, Cochrane, and Google Scholar up to April 2018. Two reviewers independently applied inclusion criteria to select potential systematic reviews of meta-analyses of randomized controlled trials (RCTs) of physical rehabilitation interventions (during subacute phase) reporting results in ADLs. Two reviewers independently extracted name of the 1st author, year of publication, physical intervention, outcome(s), total number of participants, and number of studies from each eligible meta-analysis. The number of subjects (intervention and control), ADL outcome, and effect sizes were extracted from each study. Fifty-five meta-analyses on 21 subacute rehabilitation interventions presented in 30 different publications involving a total of 314 RCTs for 13,787 subjects were identified. Standardized mean differences (SMDs), 95% confidence intervals (fixed and random effects models), 95% prediction intervals, and statistical heterogeneity (I 2 and Q test) were calculated. Virtual reality, constraint-induced movement, augmented exercises therapy, and transcranial direct current stimulation interventions resulted statistically significant (P 0.8) but with considerable heterogeneity (I2 > 75%). Only acupuncture reached “suggestive” level of evidence. Despite the range of interventions available for stroke rehabilitation in subacute phase, there is lack of high-quality evidence in meta-analyses, highlighting the need of further research reporting ADL outcomes

Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy

Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. This article presents a machine learning (ML) prediction model targeting cognitive improvement after therapy for stroke surviving patients. The prediction model relies on electronic health records from 201 ischemic stroke surviving patients containing demographic information, cognitive assessments at admission from 24 different standardized neuropsychology tests (e.g., TMT, WAIS-III, Stroop, RAVLT, etc.), and therapy information collected during rehabilitation (72,002 entries collected between March 2007 and September 2019). The study population covered young-adult patients with a mean age of 49.51 years and only 4.47% above 65 years of age at the stroke event (no age filter applied). Twenty different classification algorithms (from Python's Scikit-learn library) are trained and evaluated, varying their hyper-parameters and the number of features received as input. Best-performing models reported Recall scores around 0.7 and F1 scores of 0.6, showing the model's ability to identify patients with poor cognitive improvement. The study includes a detailed feature importance report that helps interpret the model's inner decision workings and exposes the most influential factors in the cognitive improvement prediction. The study showed that certain therapy variables (e.g., the proportion of memory and orientation executed tasks) had an important influence on the final prediction of the cognitive improvement of patients at individual and population levels. This type of evidence can serve clinicians in adjusting the therapeutic settings (e.g., type and load of therapy activities) and selecting the one that maximizes cognitive improvement

Monitoring and Prognosis System Based on the ICF for People with Traumatic Brain Injury

The objective of this research is to provide a standardized platform to monitor and predict indicators of people with traumatic brain injury using the International Classification of Functioning, Disability and Health, and analyze its potential benefits for people with disabilities, health centers and administrations. We developed a platform that allows automatic standardization and automatic graphical representations of indicators of the status of individuals and populations. We used data from 730 people with acquired brain injury performing periodic comprehensive evaluations in the years 2006-2013. Health professionals noted that the use of color-coded graphical representation is useful for quickly diagnose failures, limitations or restrictions in rehabilitation. The prognosis System achieves 41% of accuracy and sensitivity in the prediction of emotional functions, and 48% of accuracy and sensitivity in the prediction of executive functions. This monitoring and prognosis system has the potential to: (1) save costs and time, (2) provide more information to make decisions, (3) promote interoperability, (4) facilitate joint decision-making, and (5) improve policies of socioeconomic evaluation of the burden of disease. Professionals found the monitoring system useful because it generates a more comprehensive understanding of health oriented to the profile of the patients, instead of their diseases and injuries

The Impact of Body Mass Index on Functional Rehabilitation Outcomes of Working-age Inpatients with Stroke

BACKGROUND: Stroke is the most relevant cause of acquired persistent disability in adulthood. The relationship between patient’s weight during rehabilitation and stroke functional outcome is controversial, previous research reported positive, negative and no effects, with scarce studies specifically addressing working-age patients.AIM: To evaluate the association between Body Mass Index (BMI) and the functional progress of adult (\u3c65 \u3eyears) patients with stroke admitted to a rehabilitation hospital.DESIGN: Retrospective observational cohort study.SETTING: Inpatient rehabilitation center.POPULATION: 178 stroke patients (ischemic or hemorrhagic).METHODS: Point-biserial and Spearman’s correlations, multivariate linear regressions and analysis of covariance were used to describe differences in functional outcomes after adjusting for age, sex, severity, dysphagia, depression and BMI category. Functional Independence Measure (FIM), FIM gain, efficiency and effectiveness were assessed.RESULTS: Participants were separated in 3 BMI categories: normal weight (47%), overweight (33%) and obese (20%). There were no significant differences between BMI categories in any functional outcome (total FIM (TFIM), cognitive (CFIM), motor (MFIM)) at discharge, admission, gain, efficiency or effectiveness. In regression models BMI (as continuous variable) was not significant predictor of TFIM at discharge after adjusting for age, sex, severity, dysphagia, depression and ataxia (R2=0.4813), significant predictors were TFIM at admission (β = 0.528) and NIHSS (β=-0.208). MFIM efficiency did not significantly differ by BMI subgroups, neither did CFIM efficiency. Length of stay (LOS) and TFIM effectiveness were associated for normal (r=0.33) and overweight (r=0.43), but not for obese. LOS and TFIM efficiency were strongly negatively associated only for obese (r=-0.50).CONCLUSIONS: FIM outcomes were not associated to BMI, nevertheless each BMI category when individually considered (normal weight, overweight or obese) was characterized by different associations involving FIM outcomes and clinical factors. CLINICAL REHABILITATION IMPACT: In sub-acute post-stroke working-age patients undergoing rehabilitation, BMI was not associated to FIM outcomes (no obesity paradox was reported in this sample). Distinctive significant associations emerged within each BMI category, (supporting their characterization) such as length of stay and TFIM effectiveness were associated for normal weight and overweight, but not for obese. Length of stay and TFIM efficiency were strongly negatively associated only for obese

The impact of COVID-19 on home, social, and productivity integration of people with chronic traumatic brain injury or stroke living in the community

Compare community integration of people with stroke or traumatic brain injury (TBI) living in the community before and during the coronavirus severe acute respiratory syndrome coronavirus 2 disease (COVID-19) when stratifying by injury: participants with stroke (G1) and with TBI (G2); by functional independence in activities of daily living: independent (G3) and dependent (G4); by age: participants younger than 54 (G5) and older than 54 (G6); and by gender: female (G7) and male (G8) participants. Prospective observational cohort study In-person follow-up visits (before COVID-19 outbreak) to a rehabilitation hospital in Spain and on-line during COVID-19. Community dwelling adults (≥18 years) with chronic stroke or TBI. Community integration questionnaire (CIQ) the total-CIQ as well as the subscale domains (ie, home-CIQ, social-CIQ, productivity CIQ) were compared before and during COVID-19 using the Wilcoxon ranked test or paired t test when appropriate reporting Cohen effect sizes (d). The functional independence measure was used to assess functional independence in activities of daily living. Two hundred four participants, 51.4% with stroke and 48.6% with TBI assessed on-line between June 2020 and April 2021 were compared to their own in-person assessments performed before COVID-19. When analyzing total-CIQ, G1 (d = −0.231), G2 (d = −0.240), G3 (d = −0.285), G5 (d = −0.276), G6 (d = −0.199), G7 (d = −0.245), and G8 (d = −0.210) significantly decreased their scores during COVID-19, meanwhile G4 was the only group with no significant differences before and during COVID-19. In productivity-CIQ, G1 (d = −0.197), G4 (d = −0.215), G6 (d = −0.300), and G8 (d = −0.210) significantly increased their scores, meanwhile no significant differences were observed in G2, G3, G5, and G7. In social-CIQ, all groups significantly decreased their scores: G1 (d = −0.348), G2 (d = −0.372), G3 (d = −0.437), G4 (d = −0.253), G5 (d = −0.394), G6 (d = −0.319), G7 (d = −0.355), and G8 (d = −0.365). In home-CIQ only G6 (d = −0.229) significantly decreased, no significant differences were observed in any of the other groups. The largest effect sizes were observed in total-CIQ for G3, in productivity-CIQ for G6, in social-CIQ for G3 and in home-CIQ for G6 (medium effect sizes). Stratifying participants by injury, functionality, age or gender allowed identifying specific CIQ subtotals where remote support may be provided addressing them