EXCITE: Experiences of Patients with Adjuvant and Metastatic Melanoma using Disease- Specific Social Media Communities in the Advent of Novel Therapies (Preprint)

2021 ◽  
Author(s):  
Guy Faust ◽  
Alison Booth ◽  
Evie Merinopoulou ◽  
Sonia Halhol ◽  
Heena Tosar ◽  
...  
Keyword(s):  
Social Media ◽  
Targeted Therapy ◽  
Metastatic Melanoma ◽  
Language Processing ◽  
Treatment Options ◽  
Symptom Duration ◽  
Novel Treatments ◽  
Health Related ◽  
Positive Treatment ◽  
The Impact

BACKGROUND Immunotherapy and targeted therapy treatments are novel treatments available for patients with metastatic and adjuvant melanoma. As recently approved treatments, information surrounding the patients and caregiver’s experience with these therapies, perceptions of treatments, and the effect the treatments have on their day-to-day life are lacking. Such insights would be valuable for any future decision making with regards to treatment options. OBJECTIVE This study aimed to use health-related social media data to understand the experience of patients with adjuvant and metastatic melanoma who are receiving either immunotherapy or targeted therapies. This study also included caregivers’ perspectives. METHODS Publicly available social media posts by patients with self-reported adjuvant or metastatic melanoma (and their caregivers) between January 2014 to October 2019 were programmatically extracted, de-identified, cleaned and analysed using a combination of natural language processing and qualitative data analyses. This study identified spontaneously reported symptoms and their impacts, symptom duration, and the impact of treatment for both treatment groups. RESULTS Overall 1,037 users (9,023 posts) and 114 users (442 posts) were included in the metastatic group and adjuvant group, respectively. The most commonly identified symptoms in both groups were fatigue, pain or exanthema. Symptom impacts reported by both groups were physical impacts, impacts on family, and impacts on work. Positive treatment impacts were reported in both groups and covered the areas of work, social and family life, and general health and quality of life. CONCLUSIONS This study explored health-related social media to better understand the experience and perspectives of patients with melanoma receiving immunotherapy or targeted therapy treatments as well as the experience of their caregivers. This exploratory work uncovered the most commonly discussed concerns among patients and caregivers on the forums including symptoms and their impacts, thus contributing to a deeper understanding of the patient/caregiver experience. CLINICALTRIAL None

scholarly journals Health, Psychosocial, and Social issues emanating from COVID-19 pandemic based on Social Media Comments using Natural Language Processing (Preprint)

2020 ◽  
Author(s):  
Oladapo Oyebode ◽  
Chinenye Ndulue ◽  
Ashfaq Adib ◽  
Dinesh Mulchandani ◽  
Banuchitra Suruliraj ◽  
...  
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Language Processing ◽  
Social Issues ◽  
Public Perceptions ◽  
Psychosocial Issues ◽  
Social Media Data ◽  
Health Related ◽  
The Impact ◽  
Media Data

BACKGROUND The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives. In the absence of vaccines and antivirals, several behavioural change and policy initiatives, such as physical distancing, have been implemented to control the spread of the coronavirus. Social media data can reveal public perceptions toward how governments and health agencies across the globe are handling the pandemic, as well as the impact of the disease on people regardless of their geographic locations in line with various factors that hinder or facilitate the efforts to control the spread of the pandemic globally. OBJECTIVE This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data. METHODS We apply natural language processing (NLP) and thematic analysis to understand public opinions, experiences, and issues with respect to the COVID-19 pandemic using social media data. First, we collect over 47 million COVID-19-related comments from Twitter, Facebook, YouTube, and three online discussion forums. Second, we perform data preprocessing which involves applying NLP techniques to clean and prepare the data for automated theme extraction. Third, we apply context-aware NLP approach to extract meaningful keyphrases or themes from over 1 million randomly-selected comments, as well as compute sentiment scores for each theme and assign sentiment polarity (i.e., positive, negative, or neutral) based on the scores using lexicon-based technique. Fourth, we categorize related themes into broader themes. RESULTS A total of 34 negative themes emerged, out of which 15 are health-related issues, psychosocial issues, and social issues related to the COVID-19 pandemic from the public perspective. Some of the health-related issues are increased mortality, health concerns, struggling health systems, and fitness issues; while some of the psychosocial issues include frustrations due to life disruptions, panic shopping, and expression of fear. Social issues include harassment, domestic violence, and wrong societal attitude. In addition, 20 positive themes emerged from our results. Some of the positive themes include public awareness, encouragement, gratitude, cleaner environment, online learning, charity, spiritual support, and innovative research. CONCLUSIONS We uncover various negative and positive themes representing public perceptions toward the COVID-19 pandemic and recommend interventions that can help address the health, psychosocial, and social issues based on the positive themes and other remedial ideas rooted in research. These interventions will help governments, health professionals and agencies, institutions, and individuals in their efforts to curb the spread of COVID-19 and minimize its impact, as well as in reacting to any future pandemics.

scholarly journals Web discussions on cardiovascular diseases: assessing the impact of COVID-19 pandemic. An Italian prospective analysis

2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
P Magni ◽  
I Cecchini ◽  
M Fortunati ◽  
M Biroli ◽  
K Massaroni ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Limited Attention ◽  
Private Company ◽  
Entire Study ◽  
Key Factor ◽  
Health Related ◽  
Using Data ◽  
The Impact

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Non-conditioning grant from Sanofi Italia. Background. Awareness and risk knowledge are the first steps along the complex journey leading to effective primordial, primary and secondary cardiovascular disease (CVD) prevention. Interactions among people significantly influence awareness and personal beliefs and favour behavioral changes. Given the increased use of Internet to search information and share experiences on health, Web and social media represent innovative health-information-gathering sources, facilitating interactions and information exchanges able to influence health-related behaviour. Purpose. This study is based on a longitudinal Web listening analysis approach, aimed at analyzing and comparing Web discussions collected before the COVID-19 pandemic (February 2018–February 2020) with those occurred during the first pandemic wave in Italy (March–July 2020). Methods. A preliminary analysis of the data indexed by prominent search engines (i.e., Google) was performed, followed by a systematic process of data collection. Automated data identification was analyzed using data mining techniques, machine learning and Natural Language Processing (NLP) algorithms. This analysis was integrated by a qualitative analysis. Results. In Italy, over the entire study timeframe, Web conversations associated to health topics were primarily focused on vaccines and tumors, and discussions on CVDs (about 235,000 conversations) were only at the 5th place. The main topics of the conversations concerned symptoms (25%), treatments (18%), causes of disease (14%), quality of life (13%). Conversations on prevention were marginal (only 5%). During the pandemic timeframe discussion topics were focused on the growing COVID-19 risk for people with CV comorbidities, the risk linked to CVD therapies and the difficulties and delays in accessing hospitals.  Over 80% of patients were not satisfied about their caring experience and such dissatisfaction grew stronger during the pandemic. In this period, anxiety, frustration, fear and depression were the mainly mentioned feelings. A good relationship with physicians (empathy, understanding) was mentioned by patients as a key factor of a positive caring experience. Conclusions. The limited attention to CVDs and their prevention registered through the conversations on the Web is not consistent with the epidemiological relevance of such diseases in terms of health risks and mortality. Given the crucial role of Web interactions in influencing beliefs and health-related behaviours, this study highlights the urgency to promote novel prevention strategies and to engage people leveraging digital channels and social media. The COVID-19 pandemic worsened the quality of relationship and contact with physicians. Such evidence underlines further the need to develop novel models of patient management - even remotely (digital tools, telemedicine), to encourage citizen/patient involvement and empowerment.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Evaluating Cultural Impact in Discursive Space through Digital Footprints

2021 ◽  
Vol 13 (7) ◽  
pp. 4043 ◽  
Author(s):  
Jesús López Baeza ◽  
Jens Bley ◽  
Kay Hartkopf ◽  
Martin Niggemann ◽  
James Arias ◽  
...  
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Social Activity ◽  
Test Site ◽  
Cultural Discourse ◽  
Discursive Space ◽  
Topic Identification ◽  
Small Test ◽  
The Impact ◽  
The City

The research presented in this paper describes an evaluation of the impact of spatial interventions in public spaces, measured by social media data. This contribution aims at observing the way a spatial intervention in an urban location can affect what people talk about on social media. The test site for our research is Domplatz in the center of Hamburg, Germany. In recent years, several actions have taken place there, intending to attract social activity and spotlight the square as a landmark of cultural discourse in the city of Hamburg. To evaluate the impact of this strategy, textual data from the social networks Twitter and Instagram (i.e., tweets and image captions) are collected and analyzed using Natural Language Processing intelligence. These analyses identify and track the cultural topic or “people talking about culture” in the city of Hamburg. We observe the evolution of the cultural topic, and its potential correspondence in levels of activity, with certain intervention actions carried out in Domplatz. Two analytic methods of topic clustering and tracking are tested. The results show a successful topic identification and tracking with both methods, the second one being more accurate. This means that it is possible to isolate and observe the evolution of the city’s cultural discourse using NLP. However, it is shown that the effects of spatial interventions in our small test square have a limited local scale, rather than a city-wide relevance.

scholarly journals Health-Related Quality of Life in Adrenocortical Carcinoma

Cancers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1500 ◽  
Author(s):  
Rebecca V. Steenaard ◽  
Laura A. Michon ◽  
Harm R. Haak
Keyword(s):  
Quality Of Life ◽  
General Population ◽  
Adrenocortical Carcinoma ◽  
Treatment Options ◽  
Clinical Aspects ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related ◽  
The Impact

Insight into the health-related quality of life (HRQoL) impact of adrenocortical carcinoma (ACC) is important. The disease and its treatment options potentially have an impact on HRQoL. For patients with limited survival, HRQoL research is of utmost importance. We will therefore provide an overview of HRQoL studies in patients with ACC. We found six studies that measured HRQoL in 323 patients with ACC (3 cross-sectional, 1 cohort, 2 trials), all indicating a reduced HRQoL compared to the general population. The FIRMACT trial found that HRQoL of patients with ACC was reduced compared to the general population, and that chemotherapy-mitotane further reduced HRQoL even though survival improved. Clinical aspects of the disease, including cortisol and aldosterone production and adrenal insufficiency have shown great impact on HRQoL in benign disease, even after the recovery of hormonal status. However, the impact of malignant adrenal disease and treatment options on HRQoL including adrenalectomy, radiotherapy, mitotane therapy, and chemotherapy have not been sufficiently studied in patients with ACC. Although the number of HRQoL studies in patients with ACC is limited, the existing literature does indicate that ACC has a large impact on patients’ HRQoL, with disease specific aspects. Further HRQoL research in patients with ACC is essential to improve patient-centered care, preferably by using an ACC-specific HRQoL questionnaire.

scholarly journals The Emerging Therapeutic Landscape of Advanced Melanoma

2018 ◽  
Vol 24 (5) ◽  
pp. 549-558 ◽  
Author(s):  
Vanessa Henriques ◽  
Teresa Martins ◽  
Wolfgang Link ◽  
Bibiana I. Ferreira
Keyword(s):  
Skin Cancer ◽  
Metastatic Melanoma ◽  
Treatment Options ◽  
Acquired Resistance ◽  
Treatment Modalities ◽  
Specific Therapy ◽  
Novel Treatments ◽  
The Past ◽  
Therapeutic Landscape ◽  
Paradigm Shifting

Melanoma is the deadliest form of skin cancer being responsible for 80% of skin cancer deaths. Furthermore, the incidence of metastatic melanoma has increased over the past three decades with a mortality rate that continues to rise faster than most of all other cancers. The last few years have witnessed an unparalleled change in treatment options for patients with metastatic melanoma by the development of new therapeutic strategies like targeted therapies and immunotherapies that highly improved the patient’s prognosis. Despite the paradigm- shifting success of these novel treatments, their effectiveness is still limited by intrinsic or acquired resistance. The objective of this review is to provide an overview of the new available treatment modalities, criteria to select patients who might benefit from a specific therapy, mechanisms of innate and acquired resistance to these treatments and to discuss strategies to overcome drug resistance.

Using Social Media Listening to measure the impact of diseases and treatments on patients’ Health-Related Quality of Life (Preprint)

2021 ◽  
Author(s):  
Simon Renner ◽  
Tom Marty ◽  
Mickaïl Khadhar ◽  
Pierre Foulquié ◽  
Paméla Voillot ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Social Media ◽  
Health Related Quality ◽  
Web Crawler ◽  
Real World Data ◽  
Patient Centered ◽  
Related Quality ◽  
Health Related ◽  
The Impact

BACKGROUND Monitoring social media has been shown to be a useful mean to capture patients’ opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life is a useful indicator of overall patients’ health that can be captured online. OBJECTIVE This study aims to describe a Social Media Listening system which is able to detect any impact of diseases or treatments on health-related quality of life as reported in social media and forum messages written by patients. METHODS Using a web crawler, 19 health-related forums in France were harvested and messages relating a patient’s experience with a disease or a treatment were specifically collected. The algorithm was based on the two clinically validated questionnaires SF-36 and EQ-5D. Models were trained using cross-validation (a machine learning technique which obtains the best combination between different data samples) and hyperparameter optimization. Over-sampling was used to increase the infrequent dimension: after annotation, SMOTE was used to balance the proportion of the dimension among messages. RESULTS The training set was composed of 1400 messages, randomly taken from a 20 000 batch of health-related messages coming from forums. The algorithm was able to detect a general impact on health-related quality of life (sensitivity of 0.83 and specificity of 0.74), a physical impact (0.67 and 0.76), a psychic impact (0.82 and 0.60), an activity-related impact (0.73 and 0.78), a relational impact (0.73 and 0.70) and a financial impact (0.79 and 0.74). CONCLUSIONS Real-time assessment of patients’ health-related quality of life through the use of Social Media Listening is useful to a patient-centered medical care. Social media as a source of Real World Data are a complementary point of vue to understand patients’ concerns, unmet needs and how diseases and treatments can be a burden in their daily lives. Trial Registration: Not applicable (not a trial)

scholarly journals Public Perception on Healthcare Services: Evidence from Social Media Platforms in China

2019 ◽  
Vol 16 (7) ◽  
pp. 1273 ◽  
Author(s):  
Guangyu Hu ◽  
Xueyan Han ◽  
Huixuan Zhou ◽  
Yuanli Liu
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Public Perception ◽  
Content Volume ◽  
Healthcare Services ◽  
Service Improvement ◽  
Data Resource ◽  
Health Related Research ◽  
Health Related ◽  
Social Media Platforms

Social media has been used as data resource in a growing number of health-related research. The objectives of this study were to identify content volume and sentiment polarity of social media records relevant to healthcare services in China. A list of the key words of healthcare services were used to extract data from WeChat and Qzone, between June 2017 and September 2017. The data were put into a corpus, where content analyses were performed using Tencent natural language processing (NLP). The final corpus contained approximately 29 million records. Records on patient safety were the most frequently mentioned topic (approximately 8.73 million, 30.1% of the corpus), with the contents on humanistic care having received the least social media references (0.43 Million, 1.5%). Sentiment analyses showed 36.1%, 16.4%, and 47.4% of positive, neutral, and negative emotions, respectively. The doctor-patient relationship category had the highest proportion of negative contents (74.9%), followed by service efficiency (59.5%), and nursing service (53.0%). Neutral disposition was found to be the highest (30.4%) in the contents on appointment-booking services. This study added evidence to the magnitude and direction of public perceptions on healthcare services in China’s hospital and pointed to the possibility of monitoring healthcare service improvement, using readily available data in social media.

scholarly journals Consumer perceptions of using social media for health purposes: Benefits and drawbacks

2018 ◽  
Vol 25 (4) ◽  
pp. 1661-1674 ◽  
Author(s):  
Arcelio Benetoli ◽  
Timothy F Chen ◽  
Parisa Aslani
Keyword(s):  
Social Media ◽  
Health Knowledge ◽  
Information Overload ◽  
Treatment Options ◽  
Online Information ◽  
Social And Emotional ◽  
Advantages And Disadvantages ◽  
Related Information ◽  
Health Related ◽  
Use Of Social Media

Consumers are increasingly using social media to interact with other consumers about health conditions and treatment options. This study aimed to investigate the advantages and disadvantages of using social media for health-related purposes from the consumers’ perspectives. Five focus groups with 36 Australian adults with a chronic condition and on medication were conducted, audio-recorded, transcribed verbatim, and thematically analysed. Consumers reported that social media was very convenient, for accessing health-related information and for peer engagement; user-friendly; improved their health knowledge; empowered them; and provided social and emotional support. The disadvantages included information overload, wasting time; negative feelings; doubts about online information credibility; and issues related to online interactions. Despite some disadvantages, health-related use of social media led consumers to feel supported, knowledgeable, and empowered. Consumers’ motivation to keep accessing social media for health-related purposes opens up avenues for the delivery of services via social media.

scholarly journals Automated extraction of treatment patterns from social media posts: an exploratory analysis in renal cell carcinoma

2019 ◽  
Vol 15 (31) ◽  
pp. 3587-3596
Author(s):  
Sreeram V Ramagopalan ◽  
Bill Malcolm ◽  
Evie Merinopoulou ◽  
Laura McDonald ◽  
Andrew Cox
Keyword(s):  
Renal Cell Carcinoma ◽  
Social Media ◽  
Cell Carcinoma ◽  
Language Processing ◽  
Renal Cell ◽  
Treatment Patterns ◽  
The Social ◽  
Health Related ◽  
Development And Validation ◽  
Further Development

Aim: The use of health-related social media forums by patients is increasing and the size of these forums creates a rich record of patient opinions and experiences, including treatment histories. This study aimed to understand the possibility of extracting treatment patterns in an automated manner for patients with renal cell carcinoma, using natural language processing, rule-based decisions, and machine learning. Patients & methods: Obtained results were compared with those from published observational studies. Results: 42 comparisons across seven therapies, three lines of treatment, and two-time periods were made; 37 of the social media estimates fell within the variation seen across the published studies. Conclusion: This exploratory work shows that estimating treatment patterns from social media is possible and generates results within the variation seen in published studies, although further development and validation of the approach is needed.

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