From concept to practice: a scoping review of the application of AI to aphasia diagnosis and management.

PURPOSE: Aphasia is an acquired communication disability resulting from impairments in language processing following brain injury, most commonly stroke. People with aphasia experience difficulties in all modalities of language that impact their quality of life. Therefore, researchers have investigated the use of Artificial Intelligence (AI) to deliver innovative solutions in Aphasia management and rehabilitation. MATERIALS AND METHODS: We conducted a scoping review of the use of AI in aphasia research and rehabilitation to explore the evolution of AI applications to aphasia, the progression of technologies and applications. Furthermore, we aimed to identify gaps in the use of AI in Aphasia to highlight the potential areas where AI might add value. We analysed 77 studies to determine the research objectives, the history of AI techniques in Aphasia and their progression over time. RESULTS: Most of the studies focus on automated assessment using AI, with recent studies focusing on AI for therapy and personalised assistive systems. Starting from prototypes and simulations, the use of AI has progressed to include supervised machine learning, unsupervised machine learning, natural language processing, fuzzy rules, and genetic programming. CONCLUSION: Considerable scope remains to align AI technology with aphasia rehabilitation to empower patient-centred, customised rehabilitation and enhanced self-management.

Aphasia is an acquired communication disorder that impacts everyday functioning due to impairments in speech, auditory comprehension, reading, and writing.Given this communication burden, researchers have focused on utilising artificial intelligence (AI) methods for assessment, therapy and self-management.From a conceptualisation era in the early 1940s, the application of AI has evolved with significant developments in AI applications at different points in time.Despite these developments, there are ample opportunities to exploit the use of AI to deliver more advanced applications in self-management and personalising care.

Therapist Feedback and Implications on Adoption of an Artificial Intelligence-Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study.

BACKGROUND: The recent onset of the COVID-19 pandemic and the social distancing requirement have created an increased demand for virtual support programs. Advances in artificial intelligence (AI) may offer novel solutions to management challenges such as the lack of emotional connections within virtual group interventions. Using typed text from online support groups, AI can help identify the potential risk of mental health concerns, alert group facilitator(s), and automatically recommend tailored resources while monitoring patient outcomes. OBJECTIVE: The aim of this mixed methods, single-arm study was to evaluate the feasibility, acceptability, validity, and reliability of an AI-based co-facilitator (AICF) among CancerChatCanada therapists and participants to monitor online support group participants' distress through a real-time analysis of texts posted during the support group sessions. Specifically, AICF (1) generated participant profiles with discussion topic summaries and emotion trajectories for each session, (2) identified participant(s) at risk for increased emotional distress and alerted the therapist for follow-up, and (3) automatically suggested tailored recommendations based on participant needs. Online support group participants consisted of patients with various types of cancer, and the therapists were clinically trained social workers. METHODS: Our study reports on the mixed methods evaluation of AICF, including therapists' opinions as well as quantitative measures. AICF's ability to detect distress was evaluated by the patient's real-time emoji check-in, the Linguistic Inquiry and Word Count software, and the Impact of Event Scale-Revised. RESULTS: Although quantitative results showed only some validity of AICF's ability in detecting distress, the qualitative results showed that AICF was able to detect real-time issues that are amenable to treatment, thus allowing therapists to be more proactive in supporting every group member on an individual basis. However, therapists are concerned about the ethical liability of AICF's distress detection function. CONCLUSIONS: Future works will look into wearable sensors and facial cues by using videoconferencing to overcome the barriers associated with text-based online support groups. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21453.

Providing Care Beyond Therapy Sessions With a Natural Language Processing-Based Recommender System That Identifies Cancer Patients Who Experience Psychosocial Challenges and Provides Self-care Support: Pilot Study.

BACKGROUND: The negative psychosocial impacts of cancer diagnoses and treatments are well documented. Virtual care has become an essential mode of care delivery during the COVID-19 pandemic, and online support groups (OSGs) have been shown to improve accessibility to psychosocial and supportive care. de Souza Institute offers CancerChatCanada, a therapist-led OSG service where sessions are monitored by an artificial intelligence-based co-facilitator (AICF). The AICF is equipped with a recommender system that uses natural language processing to tailor online resources to patients according to their psychosocial needs. OBJECTIVE: We aimed to outline the development protocol and evaluate the AICF on its precision and recall in recommending resources to cancer OSG members. METHODS: Human input informed the design and evaluation of the AICF on its ability to (1) appropriately identify keywords indicating a psychosocial concern and (2) recommend the most appropriate online resource to the OSG member expressing each concern. Three rounds of human evaluation and algorithm improvement were performed iteratively. RESULTS: We evaluated 7190 outputs and achieved a precision of 0.797, a recall of 0.981, and an F1 score of 0.880 by the third round of evaluation. Resources were recommended to 48 patients, and 25 (52%) accessed at least one resource. Of those who accessed the resources, 19 (75%) found them useful. CONCLUSIONS: The preliminary findings suggest that the AICF can help provide tailored support for cancer OSG members with high precision, recall, and satisfaction. The AICF has undergone rigorous human evaluation, and the results provide much-needed evidence, while outlining potential strengths and weaknesses for future applications in supportive care.

What Is Autistic Burnout? A Thematic Analysis of Posts on Two Online Platforms.

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.

What was the purpose of this study?: Although the autistic community has talked about autistic burnout for a long time, there has not been much research about the topic. This study aimed to investigate autistic burnout from the perspective of autistic adults to understand what they think causes it, the symptoms and impact on their lives, and what can be done to assist prevention and recovery. Why is this an important issue?: This issue is important because autistic people have said that autistic burnout can severely affect their quality of life and well-being and contribute to poor mental health, including the risk of suicide. What did the researchers do?: We used a computer program to collect public posts from two online platforms to look at how autistic adults described autistic burnout. We collected 1127 posts shared over a 12-year period by 683 users. To understand the adults' lived experiences, we analyzed their language at the surface level and looked for common themes across the data. What were the results of the study?: The adults in this study said that autistic burnout was often first experienced during adolescence, lasted months or years, and was hard to recover from. They described severe direct and indirect consequences for their physical and mental health, capacity to function, and ability to achieve personal goals. They described a general lack of knowledge about autism, especially among health care professionals, which led to misdiagnosis and inadequate or inappropriate treatment. Masking or "camouflaging" to pass as nonautistic was the most common reason participants gave for autistic burnout. Many used strategies to manage energy levels to avoid burnout. The autistic community was an essential source of information and support for participants. Overall, stigma, discrimination, and low awareness and acceptance of autism were responsible for the cycle of autistic burnout. How do these findings add to what was already known?: As one of the first studies about autistic burnout, we learned that it happens because of factors associated with being autistic and poor autism awareness and acceptance within society. We now know that autistic people often first experience autistic burnout when they are young, but it usually recurs, which can stop autistic people leading fulfilling lives. We learned that difficulty identifying emotions may be a risk factor and that online communication may help autistic people during recovery. We found that some positive consequences of autistic burnout include autism diagnosis in adulthood, finding the autistic community, and making empowering lifestyle changes. What are the potential weaknesses in the study?: We had limited demographic information, so we do not know how diverse the sample was or how factors such as gender, age, race, or identifying as LGBTQI may have influenced some people's experience of autistic burnout. The adults in this study had access to online platforms and could communicate in writing, and so, people with higher communication support needs may not have been included. How will these recommendations help autistic adults now or in the future?: The findings reinforce the personal stories of autistic people and show that autistic burnout is a common, consistent, and harmful experience. The findings show it is vital for health professionals to recognize autistic burnout to provide appropriate care and support because prevention and early detection could help stop the harmful cycle of autistic burnout. The findings underscore the importance of reducing discrimination and stigma against autistic people and increased acceptance.

Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence.

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 recovery and understanding psychological impact via emotion changes, and they could potentially inform health care decision making. This study exploited AI and social media to enhance our understanding of human behaviors in global emergencies, which could lead to improved planning and policy making for future crises.

Natural Language Processing-Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study.

BACKGROUND: Cancer and its treatment can significantly impact the short- and long-term psychological well-being of patients and families. Emotional distress and depressive symptomatology are often associated with poor treatment adherence, reduced quality of life, and higher mortality. Cancer support groups, especially those led by health care professionals, provide a safe place for participants to discuss fear, normalize stress reactions, share solidarity, and learn about effective strategies to build resilience and enhance coping. However, in-person support groups may not always be accessible to individuals; geographic distance is one of the barriers for access, and compromised physical condition (eg, fatigue, pain) is another. Emerging evidence supports the effectiveness of online support groups in reducing access barriers. Text-based and professional-led online support groups have been offered by Cancer Chat Canada. Participants join the group discussion using text in real time. However, therapist leaders report some challenges leading text-based online support groups in the absence of visual cues, particularly in tracking participant distress. With multiple participants typing at the same time, the nuances of the text messages or red flags for distress can sometimes be missed. Recent advances in artificial intelligence such as deep learning-based natural language processing offer potential solutions. This technology can be used to analyze online support group text data to track participants' expressed emotional distress, including fear, sadness, and hopelessness. Artificial intelligence allows session activities to be monitored in real time and alerts the therapist to participant disengagement. OBJECTIVE: We aim to develop and evaluate an artificial intelligence-based cofacilitator prototype to track and monitor online support group participants' distress through real-time analysis of text-based messages posted during synchronous sessions. METHODS: An artificial intelligence-based cofacilitator will be developed to identify participants who are at-risk for increased emotional distress and track participant engagement and in-session group cohesion levels, providing real-time alerts for therapist to follow-up; generate postsession participant profiles that contain discussion content keywords and emotion profiles for each session; and automatically suggest tailored resources to participants according to their needs. The study is designed to be conducted in 4 phases consisting of (1) development based on a subset of data and an existing natural language processing framework, (2) performance evaluation using human scoring, (3) beta testing, and (4) user experience evaluation. RESULTS: This study received ethics approval in August 2019. Phase 1, development of an artificial intelligence-based cofacilitator, was completed in January 2020. As of December 2020, phase 2 is underway. The study is expected to be completed by September 2021. CONCLUSIONS: An artificial intelligence-based cofacilitator offers a promising new mode of delivery of person-centered online support groups tailored to individual needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/21453.

Cancer in Lockdown: Impact of the COVID-19 Pandemic on Patients with Cancer.

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.

Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories.

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 increase in emotional wellbeing, indicating a decrease in psychological distress. It is opportune to further investigate AI in OCSG for early psychological intervention as an adjunct to conventional intervention programs.

Finding the Intersection of Neuroplasticity, Stroke Recovery, and Learning: Scope and Contributions to Stroke Rehabilitation.

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.

Machine learning to support social media empowered patients in cancer care and cancer treatment decisions.

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 seeking behaviours to providers of information and emotional support to other patients was also observed. CONCLUSIONS: Findings 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.

Robotic-assisted vs. open radical prostatectomy: A machine learning framework for intelligent analysis of patient-reported outcomes from online cancer support groups.

BACKGROUND: The advantages of Robot-assisted laparoscopic prostatectomy (RARP) over open radical prostatectomy (ORP) in Prostate cancer perioperatively are well-established, but quality of life is more contentious. Increasingly, patients are utilising online cancer support groups (OCSG) to express themselves. Currently there is no method of analysis of these sophisticated data sources. We have used the PRIME-2 (Patient Reported Information Multidimensional Exploration version 2) framework for automated identification and intelligent analysis of decision-making, functional and emotional outcomes in men undergoing ORP vs. RARP from OCSG discussions. METHODS: The PRIME-2 framework was developed to retrospectively analyse individualised patient-reported information from 5,157 patients undergoing RARP and 579 ORP. The decision factors, side effects, and emotions in 2 groups were analysed and compared using Chi-squared, t tests, and Pearson correlation. RESULTS: There were no differences in Gleason score, Prostate Specific Antigen (PSA), and age between the groups. Surgeon experience and preservation of erectile function (P < 0.01) were important factors in the decision making process. There were no significant differences in urinary, sexual, or bowel symptoms between ORP and RARP on a monthly basis during the initial 12 months. Emotions expressed by patients undergoing RARP were more consistent and positive while ORP expressed more negative emotions at the time of surgery and 3 months postsurgery (P < 0.05), due to pain and discomfort, and during ninth month due to fear and anxiety of pending PSA tests. CONCLUSIONS: ORP and RARP demonstrated similar side effect profiles for 12 months, but PRIME-2 enables identification of important quality of life features and emotions over time. It is timely for clinicians to accept OCSG as an adjunct to Prostate cancer care.

The Patient-Reported Information Multidimensional Exploration (PRIME) Framework for Investigating Emotions and Other Factors of Prostate Cancer Patients with Low Intermediate Risk Based on Online Cancer Support Group Discussions.

BACKGROUND: This study aimed to use the Patient Reported Information Multidimensional Exploration (PRIME) framework, a novel ensemble of machine-learning and deep-learning algorithms, to extract, analyze, and correlate self-reported information from Online Cancer Support Groups (OCSG) by patients (and partners of patients) with low intermediate-risk prostate cancer (PCa) undergoing radical prostatectomy (RP), external beam radiotherapy (EBRT), and active surveillance (AS), and to investigate its efficacy in quality-of-life (QoL) and emotion measures. METHODS: From patient-reported information on 10 OCSG, the PRIME framework automatically filtered and extracted conversations on low intermediate-risk PCa with active user participation. Side effects as well as emotional and QoL outcomes for 6084 patients were analyzed. RESULTS: Side-effect profiles differed between the methods analyzed, with men after RP having more urinary and sexual side effects and men after EBRT having more bowel symptoms. Key findings from the analysis of emotional expressions showed that PCa patients younger than 40 years expressed significantly high positive and negative emotions compared with other age groups, that partners of patients expressed more negative emotions than the patients, and that selected cohorts (< 40 years, > 70 years, partners of patients) have frequently used the same terms to express their emotions, which is indicative of QoL issues specific to those cohorts. CONCLUSION: Despite recent advances in patient-centerd care, patient emotions are largely overlooked, especially in younger men with a diagnosis of PCa and their partners. The authors present a novel approach, the PRIME framework, to extract, analyze, and correlate key patient factors. This framework improves understanding of QoL and identifies low intermediate-risk PCa patients who require additional support.