Natural language processing of Reddit data to evaluate dermatology patient experiences and therapeutics

From Web to RheumaLpack: Creating a Linguistic Corpus for Exploitation and Knowledge Discovery in Rheumatology

This study introduces RheumaLinguisticpack (RheumaLpack), the first specialised linguistic web corpus designed for the field of musculoskeletal disorders. By combining web mining (i.e., web scraping) and natural language processing (NLP) techniques, as well as clinical expertise, RheumaLpack systematically captures and curates structured and unstructured data across a spectrum of web sources including clinical trials registers (i.e., ClinicalTrials.gov), bibliographic databases (i.e., PubMed), medical agencies (i.e. European Medicines Agency), social media (i.e., Reddit), and accredited health websites (i.e., MedlinePlus, Harvard Health Publishing, and Cleveland Clinic). Given the complexity of rheumatic and musculoskeletal diseases (RMDs) and their significant impact on quality of life, this resource can be proposed as a useful tool to train algorithms that could mitigate the diseases' effects. Therefore, the corpus aims to improve the training of artificial intelligence (AI) algorithms and facilitate knowledge discovery in RMDs. The development of RheumaLpack involved a systematic six-step methodology covering data identification, characterisation, selection, collection, processing, and corpus description. The result is a non-annotated, monolingual, and dynamic corpus, featuring almost 3 million records spanning from 2000 to 2023. RheumaLpack represents a pioneering contribution to rheumatology research, providing a useful resource for the development of advanced AI and NLP applications. This corpus highlights the value of web data to address the challenges posed by musculoskeletal diseases, illustrating the corpus's potential to improve research and treatment paradigms in rheumatology. Finally, the methodology shown can be replicated to obtain data from other medical specialities. The code and details on how to build RheumaLpack are also provided to facilitate the dissemination of such resource.

Natural language processing in dermatology: A systematic literature review and state of the art

BACKGROUND

Natural Language Processing (NLP) is a field of both computational linguistics and artificial intelligence (AI) dedicated to analysis and interpretation of human language.

OBJECTIVES

This systematic review aims at exploring all the possible applications of NLP techniques in the dermatological setting.

METHODS

Extensive search on 'natural language processing' and 'dermatology' was performed on MEDLINE and Scopus electronic databases. Only journal articles with full text electronically available and English translation were considered. The PICO (Population, Intervention or exposure, Comparison, Outcome) algorithm was applied to our study protocol.

RESULTS

Natural Language Processing (NLP) techniques have been utilized across various dermatological domains, including atopic dermatitis, acne/rosacea, skin infections, non-melanoma skin cancers (NMSCs), melanoma and skincare. There is versatility of NLP in data extraction from diverse sources such as electronic health records (EHRs), social media platforms and online forums. We found extensive utilization of NLP techniques across diverse dermatological domains, showcasing its potential in extracting valuable insights from various sources and informing diagnosis, treatment optimization, patient preferences and unmet needs in dermatological research and clinical practice.

CONCLUSIONS

While NLP shows promise in enhancing dermatological research and clinical practice, challenges such as data quality, ambiguity, lack of standardization and privacy concerns necessitate careful consideration. Collaborative efforts between dermatologists, data scientists and ethicists are essential for addressing these challenges and maximizing the potential of NLP in dermatology.

Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making

INTRODUCTION

While pharmaceutical companies aim to leverage real-world data (RWD) to bridge the gap between clinical drug development and real-world patient outcomes, extant research has mainly focused on the use of social media in a post-approval safety-surveillance setting. Recent regulatory and technological developments indicate that social media may serve as a rich source to expand the evidence base to pre-approval and drug development activities. However, use cases related to drug development have been largely omitted, thereby missing some of the benefits of RWD. In addition, an applied end-to-end understanding of RWD rooted in both industry and regulations is lacking.

OBJECTIVE

We aimed to investigate how social media can be used as a source of RWD to support regulatory decision making and drug development in the pharmaceutical industry. We aimed to specifically explore the data pipeline and examine how social-media derived RWD can align with regulatory guidance from the US Food and Drug Administration and industry needs.

METHODS

A machine learning pipeline was developed to extract patient insights related to anticoagulants from X (Twitter) data. These findings were then analysed from an industry perspective, and complemented by interviews with professionals from a pharmaceutical company.

RESULTS

The analysis reveals several use cases where RWD derived from social media can be beneficial, particularly in generating hypotheses around patient and therapeutic area needs. We also note certain limitations of social media data, particularly around inferring causality.

CONCLUSIONS

Social media display considerable potential as a source of RWD for guiding efforts in pharmaceutical drug development and pre-approval settings. Although further regulatory guidance on the use of social media for RWD is needed to encourage its use, regulatory and technological developments are suggested to warrant at least exploratory uses for drug development.

What Do My (Online) Friends Think? A Topic Modeling Approach to Identifying Patterns of Response to Self-Injurious Behaviors on Reddit

OBJECTIVE

Approximately 17% of adolescents and young adults will engage in non-suicidal self-injury (NSSI) at least once in their lifetime, leading the World Health Organization to identify self-injury as one of the top five public health concerns for adolescents. Despite the widespread prevalence of this behavior, NSSI continues to be heavily stigmatized in both medical and community settings, deterring many engaged in NSSI from seeking informal support from friends and family as well as formal psychological or psychiatric treatment. In contrast to the low rates of in-person help-seeking for NSSI, online support groups are highly utilized by those engaged in NSSI. Thus, an empirical study of responses to frequent, voluntary disclosure of NSSI on social media is needed to better understand how these communities meet the needs of those who self-injure.

METHOD

The current project used latent Dirichlet allocation to identify frequent and favored themes in response to self-injury content in the largest self-injury group on Reddit (over 100,000 members). Reddit, the 9th most visited website in the world, is a chat-based social media platform that has 430+ million active users and billions of site visits, with current estimates suggesting that ∼63% of the U.S. population are Reddit users.

RESULTS

Identified themes included: (1) recovery encouragement; (2) provision of social and instrumental support; and (3) daily realities of living with NSSI. Responses that encouraged recovery received more upvotes on Reddit than any other type of comment.

CONCLUSION

These results can inform evidence-based, person-centered, dimensional treatments for NSSI.

Revealing patient-reported experiences in healthcare from social media using thedesign-acquire-process-model-analyse-visualise framework

Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centred care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients' journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this article, we introduce the design-acquire-process-model-analyse-visualise framework to provide an overview of techniques and an approach to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework's value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses.

Treatment options and emotional well-being in patients with rosacea: An unsupervised machine learning analysis of over 200,000 posts

Background

Many patients with rosacea join online support groups to gather and disseminate information about disease management and provide emotional support for others.

Objective

To better understand rosacea patient's primary concerns for the disease as well as their disease search patterns online.

Methods

Overall, 207,038 posts by 41,400 users were collected from June 1, 2017, to June 1, 2022, in a popular online forum. We applied Latent Dirichlet Allocation (LDA), an unsupervised machine learning model, to organize the posts into topics. Keywords for each topic supplied by LDA were used to manually assign topic and category labels.

Results

Twenty-three significant topics of conversation were identified and organized into 4 major categories, including Management (50.33%), Clinical Presentation (24.14%), Emotion (21.97%), and Information Appraisal (3.57%).

Limitations

Although we analyzed the largest forum on the internet for rosacea, generalizability is limited given the presence of other smaller forums and the skewed demographics of forum users.

Conclusion

Social media forums play an important role for disease discussion and emotional venting. Although rosacea management was the most frequently discussed topic, emotional posting was a significantly prevalent occurrence.

Scoping the Priorities and Concerns of Parents: Infodemiology Study of Posts on Mumsnet and Reddit

BACKGROUND

Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiaries. Web-based forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and well-being of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation.

OBJECTIVE

The aim of the study is to identify common concerns expressed on 2 major web-based forums and cluster these to identify potential family health concern topics as indicative priority areas for future research and innovation.

METHODS

We text-mined the r/Parenting subreddit (69,846 posts) and the parenting section of Mumsnet (99,848 posts) to create a large corpus of posts. A generative statistical model (latent Dirichlet allocation) was used to identify the most discussed topics in the corpus, and content analysis was applied to identify the parenting concerns found in a subset of posts.

RESULTS

A model with 25 topics produced the highest coherence and a wide range of meaningful parenting concern topics. The most frequently expressed parenting concerns are related to their child's sleep, self-care, eating (and food), behavior, childcare context, and the parental context including parental conflict. Topics directly associated with infants, such as potty training and bottle feeding, were more common on Mumsnet, while parental context and screen time were more common on r/Parenting.

CONCLUSIONS

Latent Dirichlet allocation topic modeling can be applied to gain a rapid, yet meaningful overview of parent concerns expressed on a large and diverse set of social media posts and used to complement traditional insight gathering methods. Parents framed their concerns in terms of children's everyday health concerns, generating topics that overlap significantly with established family health concern topics. We provide evidence of the range of family health concerns found at these sources and hope this can be used to generate material for use alongside traditional insight gathering methods.

What can we learn from a Chinese social media used by glaucoma patients?

PURPOSE

Our study aims to discuss glaucoma patients' needs and Internet habits using big data analysis and Natural Language Processing (NLP) based on deep learning (DL).

METHODS

In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glaucoma bar of Baidu Tieba, China. According to the contents of topic posts, we classified them into posts with seeking medical advice and without seeking medical advice (social support, expressing emotions, sharing knowledge, and others). Word Cloud and frequency statistics were used to analyze the contents and visualize the keywords of topic posts. Two DL models, Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT), were trained to identify the posts seeking medical advice. The evaluation matrices included: accuracy, F1 value, and the area under the ROC curve (AUC).

RESULTS

A total of 10,892 topic posts were included, among them, most were seeking medical advice (N = 7071, 64.91%), and seeking advice regarding symptoms or examination (N = 4913, 45.11%) dominated the majority. The following were searching for social support (N = 2362, 21.69%), expressing emotions (N = 497, 4.56%), and sharing knowledge (N = 527, 4.84%) in sequence. The word cloud analysis results showed that ocular pressure, visual field, examination, and operation were the most frequent words. The accuracy, F1 score, and AUC were 0.891, 0.891, and 0.931 for the BERT model, 0.82, 0.821, and 0.890 for the Bi-LSTM model.

CONCLUSION

Social media can help enhance the patient-doctor relationship by providing patients' concerns and cognition about glaucoma in China. NLP can be a powerful tool to reflect patients' focus on diseases. DL models performed well in classifying Chinese medical-related texts, which could play an important role in public health monitoring.

Integrating unsupervised and supervised learning techniques to predict traumatic brain injury: A population-based study

This work aimed to identify pre-existing health conditions of patients with traumatic brain injury (TBI) and develop predictive models for the first TBI event and its external causes by employing a combination of unsupervised and supervised learning algorithms. We acquired up to five years of pre-injury diagnoses for 488,107 patients with TBI and 488,107 matched control patients who entered the emergency department or acute care hospitals between April 1st, 2002, and March 31st, 2020. Diagnoses were obtained from the Ontario Health Insurance Plan (OHIP) database which contains province-wide claims data by physicians in Ontario, Canada for inpatient and outpatient services. A screening process was conducted on the OHIP diagnostic codes to limit the subsequent analysis to codes that were predictive of TBI, which concluded that 314 codes were significantly associated with TBI. The Latent Dirichlet Allocation (LDA) model was applied to the diagnostic codes and generated an optimal number of 19 topics that concur with published literature but also suggest other unexplored areas. Estimated word-topic probabilities from the LDA model helped us detect pre-morbid conditions among patients with TBI by uncovering the underlying patterns of diagnoses, meanwhile estimated document-topic probabilities were utilized in variable creation as form of a dimension reduction. We created 19 topic scores for each patient in the cohort which were utilized along with socio-demographic factors for Random Forest binary classifier models. Test set performances evaluated using area under the receiver operating characteristic curve (AUC) were: TBI event (AUC = 0.85), external cause of injury: falls (AUC = 0.85), struck by/against (AUC = 0.83), cyclist collision (AUC = 0.76), motor vehicle collision (AUC = 0.83). Our analysis successfully demonstrated the feasibility of using machine learning to predict TBI due to various external causes and identified the most important factors that contribute to this prediction.

Principles, applications, and future of artificial intelligence in dermatology

This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption. Additionally, we discuss how the future of AI in dermatology might evolve and the emerging paradigm of large language, and multi-modal models to emphasize the importance of developing responsible, fair, and equitable models in dermatology.

Natural Language Processing for Large-Scale Analysis of Eczema and Psoriasis Social Media Comments

Social media tools are widely used by dermatologic patients. Eczema and psoriasis, two of the most common inflammatory skin diseases, are well-represented on the social media site Reddit. We used natural language processing tools to examine comments in subreddits r/psoriasis and r/eczema (combined user base >187,000), tracking commenters' interest levels and sentiments related to common treatments for psoriasis and eczema as well as discussions of adverse drug reactions. All comments from 2014-2020 from the subreddits r/eczema (n = 196,571) and r/psoriasis (n = 123,144) were retrieved and processed using natural language processing tools. Comment volume in r/eczema related to antibacterial therapies, lifestyle changes, and prednisone decreased from 2014-2020, whereas phototherapy comments remained stable, and dupilumab comment volume increased. Comment volume in r/psoriasis for newer therapeutics (including biologics and apremilast) increased after Food and Drug Administration approval, whereas older therapies such as etanercept, adalimumab, and methotrexate decreased over time. Sentiment scores tended to decrease in the years after Food and Drug Administration approval. Among psoriasis treatments, calcipotriene and branded calcipotriene/betamethasone foam had the highest sentiment, whereas apremilast had the lowest overall sentiment score. These analyses also identified changes in patient interest levels and sentiment related to eczema and psoriasis treatments, suggesting an area for additional research.

Comparing Literature- and Subreddit-Derived Laboratory Values in Polycystic Ovary Syndrome (PCOS): Validation of Clinical Data Posted on PCOS Reddit Forums

BACKGROUND

Polycystic ovary syndrome (PCOS) is a heterogeneous condition that affects 4% to 21% of people with ovaries. Inaccessibility or dissatisfaction with clinical treatment for PCOS has led to some individuals with the condition discussing their experiences in specialized web-based forums.

OBJECTIVE

This study explores the feasibility of using such web-based forums for clinical research purposes by gathering and analyzing laboratory test results posted in an active PCOS forum, specifically the PCOS subreddit hosted on Reddit.

METHODS

We gathered around 45,000 posts from the PCOS subreddit. A random subset of 5000 posts was manually read, and the presence of laboratory test results was labeled. These labeled posts were used to train a machine learning model to identify which of the remaining posts contained laboratory results. The laboratory results were extracted manually from the identified posts. These self-reported laboratory test results were compared with values in the published literature to assess whether the results were concordant with researcher-published values for PCOS cohorts. A total of 10 papers were chosen to represent published PCOS literature, with selection criteria including the Rotterdam diagnostic criteria for PCOS, a publication date within the last 20 years, and at least 50 participants with PCOS.

RESULTS

Overall, the general trends observed in the laboratory test results from the PCOS web-based forum were consistent with clinically reported PCOS. A number of results, such as follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone, were concordant with published values for patients with PCOS. The high consistency of these results among the literature and when compared to the subreddit suggests that follicle stimulating hormone, fasting insulin, and anti-Mullerian hormone are more consistent across PCOS phenotypes than other test results. Some results, such as testosterone, sex hormone-binding globulin, and homeostasis model assessment-estimated insulin resistance index, were between those of PCOS literature values and normal values, as defined by clinical testing limits. Interestingly, other results, including dehydroepiandrosterone sulfate, luteinizing hormone, and fasting glucose, appeared to be slightly more dysregulated than those reported in the literature.

CONCLUSIONS

The differences between the forum-posted results and those published in the literature may be due to the selection process in clinical studies and the possibility that the forum disproportionally describes PCOS phenotypes that are less likely to be alleviated with medical intervention. However, the degree of concordance in most laboratory test values implied that the PCOS web-based forum participants were representative of research-identified PCOS cohorts. This validation of the PCOS subreddit grants the possibility for more research into the contents of the subreddit and the idea of undertaking similar research using the contents of other medical internet forums.

A systematic review of the use of topic models for short text social media analysis

Recently, research on short text topic models has addressed the challenges of social media datasets. These models are typically evaluated using automated measures. However, recent work suggests that these evaluation measures do not inform whether the topics produced can yield meaningful insights for those examining social media data. Efforts to address this issue, including gauging the alignment between automated and human evaluation tasks, are hampered by a lack of knowledge about how researchers use topic models. Further problems could arise if researchers do not construct topic models optimally or use them in a way that exceeds the models' limitations. These scenarios threaten the validity of topic model development and the insights produced by researchers employing topic modelling as a methodology. However, there is currently a lack of information about how and why topic models are used in applied research. As such, we performed a systematic literature review of 189 articles where topic modelling was used for social media analysis to understand how and why topic models are used for social media analysis. Our results suggest that the development of topic models is not aligned with the needs of those who use them for social media analysis. We have found that researchers use topic models sub-optimally. There is a lack of methodological support for researchers to build and interpret topics. We offer a set of recommendations for topic model researchers to address these problems and bridge the gap between development and applied research on short text topic models.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10462-023-10471-x.

Reddit Users' Questions and Concerns about Anesthesia

Background: Patients utilize social media in search of support networks. Reddit is one of the most popular social media sites and allows users to anonymously connect. Anesthesia patients are actively using Reddit to discuss their treatment options and experiences within the medical system. Methods: Posts published on an active Reddit forum on Anesthesia (i.e., /r/Anesthesia) were used. Big Query was used to collect posts from /r/Anesthesia. We collected 3,288 posts published between December 2015 and August 2019. We collected a control group of 3,288 posts from a Reddit forum not related to Anesthesia. Using latent Dirichlet allocation (LDA) we extracted 20 topics from our data set. The LDA topic themes most associated with posts in /r/Anesthesia compared to the control group were determined. Results: LDA analysis of posts in /r/Anesthesia relative to a control group produced 6 distinct categories of posts (Table 1). The posts most associated with /r/Anesthesia when compared to a control group were posts belonging to the “Physician-Patient Experience” category (Cohen’s d= 0.389) while the posts least associated with /r/Anesthesia were from the “Uncertainties” category of posts (Cohen’s d= 0.147). Example experiences from members of the /r/Anesthesia forum highlight subjective experiences of patients undergoing anesthesia. Conclusions: The language used on social media can provide insights into an individual's experience with anesthesia and inform physicians about patient concerns. Anesthesiologists are poised to address these concerns and prevent anonymous misinformation by providing verified physician insights on the forum /r/Anesthesia.

From the exotic to the everyday: The Avocado crossing borders via cyberspace

With the globalization of food sales and consumption, exotic foods are now regularly crossing geographical and cultural borders and moving into local areas. This process is attracting ever-increasing attention from academics. Taking avocado consumption presented on Sina Weibo as an example, this research analyzes avocado related user-generated content on Sina Weibo over three years- 2013, 2015, and 2017- and employs topic modeling and semantic network methods to obtain the mechanism by which exotic food cross borders to appear in local consumers' daily food choices. Two specific links are explored: online information dissemination and offline daily consumption. The result indicates that a selective geographical narrative and framework for avocado information influence local consumers' choice of exotic foods according to three aspects: edibility, accessibility, and acceptability. For local consumers, the avocado is now connected with local objects and spaces, gradually transforming from a novelty to functional daily food and from low to high-frequency consumption to high-frequency consumption, escaping the marginal and penetrating into the core cultural context and completing the process of embedment into the everyday. This study refutes the assertion that "globalized diets bring about homogenized diets," explores the mechanism of influence by which information dissemination in cyberspace affects cultural borders, complements the study of food consumption in Southern countries, and provides new thoughts on the theoretical and practical exploration of food globalization from the perspective of food geography.

Glaucoma-related posts from a Chinese social media: An exploratory study.. [DOI: 10.21203/rs.3.rs-2312218/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Purpose: Our study aims to discuss glaucoma patients' needs and Internet habits using big data analysis and Natural Language Processing (NLP) based on deep learning (DL). We also developed and validated DL models to recognize social media data. Methods: In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glaucoma bar of Baidu Tieba. According to the contents of topic posts, we classified them into posts with or without seeking medical advice. Word Cloud and frequency statistics were used to analyze the contents and visualize the keywords. Two DL models, Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT), were trained to identify the posts seeking medical advice. The evaluation matrices included: accuracy, F1 value, and the area under the ROC curve (AUC). Results: A total of 10,892 topic posts were included, among them, most were seeking medical advice (N=7071, 64.91%), and seeking advice regarding symptoms or examination (N=4913, 45.11%) dominated the majority, followed by searching for social support , expressing emotions, and sharing knowledge. The word cloud analysis showed that ocular pressure, visual field, examination, and operation were the most frequent words. The accuracy, F1 score, and AUC were 0.891, 0.891, and 0.931 for BERT model, 0.82, 0.821, and 0.890 for Bi-LSTM model. Conclusion: Social media can help enhance the patient-doctor relationship by providing patients’ concerns and cognition about glaucoma. DL models performed well in classifying Chinese medical-related texts, which could play an important role in public health monitoring.

Quantitative Patient-Reported Experience Measures Derived From Natural Language Processing Have a Normal Distribution and No Ceiling Effect

BACKGROUND AND OBJECTIVES

Patient-reported experience measures have the potential to guide improvement in health care delivery. Many patient-reported experience measures are limited by the presence of strong ceiling effects that limit their analytical utility.

METHODS

We used natural language processing to develop 2 new methods of evaluating patient experience using text comments and associated ordinal and categorical ratings of willingness to recommend from 1390 patients receiving specialty or nonspecialty care at our offices. One method used multivariable analysis based on linguistic factors to derive a formula to estimate the ordinal likelihood to recommend. The other method used the meaning extraction method of thematic analysis to identify words associated with categorical ratings of likelihood to recommend with which we created an equation to compute an experience score. We measured normality of the 2 score distributions and ceiling effects.

RESULTS

Spearman rank-order correlation analysis identified 36 emotional and linguistic constructs associated with ordinal rating of likelihood to recommend, 9 of which were independently associated in multivariable analysis. The calculation derived from this model corresponded with the original ordinal rating with an accuracy within 0.06 units on a 0 to 10 scale. This score and the score developed from thematic analysis both had a relatively normal distribution and limited or no ceiling effect.

CONCLUSIONS

Quantitative ratings of patient experience developed using natural language processing of text comments can have relatively normal distributions and no ceiling effect.

Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak

Forecasting the stock market is one of the most difficult undertakings in the financial industry due to its complex, volatile, noisy, and nonparametric character. However, as computer science advances, an intelligent model can help investors and analysts minimize investment risk. Public opinion on social media and other online portals is an important factor in stock market predictions. The COVID-19 pandemic stimulates online activities since individuals are compelled to remain at home, bringing about a massive quantity of public opinion and emotion. This research focuses on stock market movement prediction with public sentiments using the long short-term memory network (LSTM) during the COVID-19 flare-up. Here, seven different sentiment analysis tools, VADER, logistic regression, Loughran–McDonald, Henry, TextBlob, Linear SVC, and Stanford, are used for sentiment analysis on web scraped data from four online sources: stock-related articles headlines, tweets, financial news from "Economic Times" and Facebook comments. Predictions are made utilizing both feeling scores and authentic stock information for every one of the 28 opinion measures processed. An accuracy of 98.11% is achieved by using linear SVC to calculate sentiment ratings from Facebook comments. Thereafter, the four estimated sentiment scores from each of the seven instruments are integrated with stock data in a step-by-step fashion to determine the overall influence on the stock market. When all four sentiment scores are paired with stock data, the forecast accuracy for five out of seven tools is at its most noteworthy, with linear SVC computed scores assisting stock data to arrive at its most elevated accuracy of 98.32%.

Ophthalmology Inquiries on Reddit: What Should Physicians Know?

Purpose

Patients are seeking answers to personal medical questions on social media. Reddit, a popular social media site, has been overlooked as a source of data in the field of ophthalmology. We analyzed posts in the subreddit, r/Ophthalmology, to better understand the most common ophthalmic conditions patients are discussing online and how often those making posts are being advised to seek professional medical care.

Patients and Methods

This cross-sectional study analyzed posts and comments from March 18, 2018 to November 9, 2020. All posts and comments on r/Ophthalmology are public and were accessed using the Python Reddit API Wrapper. This text was analyzed for unique references to common ophthalmic conditions and for mentions and recommendations to different types of medical care.

Results

Nine hundred and nineteen posts were collected from the above timeframe. An auto-moderator makes a comment on every post to discourage patients from asking personal medical questions. Despite this, over two-thirds of posts discussed medical treatment for eye conditions in either a post, comment, or both. Almost half of all posts mention “ophthalmology”, but only 9% go as far as to recommend ophthalmic care. One-third of posts have no reference to medical care. Within posts, flashes and floaters were the most common condition mentioned, making up 15% of unique mentions, followed by glaucoma (7.4%) and retinal detachments (6.7%). Within comments, cataracts were most commonly discussed, making up 12% of unique mentions, followed by glaucoma (8.9%).

Conclusion

These findings show that patients are seeking information about their eye health on the r/Ophthalmology subreddit and that Reddit users are engaging with these types of posts, instead of recommending professional care in most cases. It is important for ophthalmologists to recognize the most common conditions patients are asking about online and learn how they can do a better job of educating their patients in the office.

Benefits, drawbacks, and challenges of social media use in dermatology: A systematic review

The presence of dermatological information on social media has grown exponentially over the last two decades. Consequently, the recent literature on this topic is abundant. Many authors have highlighted that social media constitutes a unique opportunity for patient education. Additionally, numerous other benefits of these platforms have been reported. However, other authors have focused on the potential risks that these networks involve. The main concerns are patient confidentiality, legal considerations and ethical issues. Therefore, we stand at a crossroads where the many advantages of social media use in dermatology seem to be underestimated due to the presence of potential drawbacks. At this point, we propose that a systematic review of the positive and negative aspects of using social media in dermatology is necessary. We carried out a comprehensive systematic review dating from inception to July 2021. Finally, 161 articles were included. Fifteen benefits, 11 drawbacks and 10 challenges of social media use in dermatology were identified and discussed. Suggested strategies to address the identified drawbacks were provided. Overall, while there are risks to using social media, they are outnumbered by their benefits. Therefore, dermatologists should embrace this opportunity to educate patients and aim to create rigorous and engaging content.

A thematic cluster analysis of parents' online discussions about fussy eating

Food fussiness is associated with non‐responsive parent feeding practices, such as persuasive and instrumental feeding. Although most children described as ‘fussy eaters’ are likely exhibiting developmentally typical behaviours, up to half of the parents of children 2–5 years old express concerns. Concern for fussy eating may mediate the use of non‐responsive feeding practices and so must be addressed in parent feeding interventions. Therefore, it is critical to better understand parents' concerns and how they may relate to feeding practices. This study aimed to explore how parents' feeding practices and the social cognitive factors that may drive them clustered based on parents' concern for fussy eating. Data were collected from parent discussions of fussy eating on a Reddit forum (80,366 posts). Latent Dirichlet allocation was used to identify discussions of fussy eating. Relevant posts (1542) made by users who identified as a parent of a fussy eater (n = 630) underwent qualitative coding and thematic analysis. Five clusters of parents were identified, ranging in size from 53 to 189 users. These were primarily characterised by parents' degree of concern and feeding practices: (1) High concern, nonresponsive; (2) Concerned, nonresponsive; (3) Low concern, responsive; (4) Low concern, mixed strategies; (5) Low concern, indulgent. Parents who used responsive practices tended to be less concerned for fussy eating, have greater trust in their child's ability to self‐regulate hunger, have longer‐term feeding goals, and exhibit greater ability for personal self‐regulation. Future research should further examine how these constructs may be leveraged in parent feeding interventions.

Parents using nonresponsive practices to manage fussy eating may have more concerns about fussy eating, less trust in their child's ability to self‐regulate hunger, shorter‐term feeding goals, and less developed self‐regulation skills.

As most children described as “fussy eaters” are likely exhibiting developmentally typical behaviours, interventions addressing nutrition and feeding practices in early childhood should aim to alleviate parental concern for fussy eating, for example, by helping them recognise developmentally normal eating behaviours.

Early childhood feeding interventions may also be enhanced by helping parents to develop long‐term feeding goals and improved self‐regulation skills in the context of their feeding practices.

Persistence and Attrition among Participants in a Multi-Page Online Survey Recruited via Reddit’s Social Media Network

Participant attrition is a major concern for the validity of longer or complex surveys. Unlike paper-based surveys, which may be discarded even if partially completed, multi-page online surveys capture responses from all completed pages until the time of abandonment. This can result in different item response rates, with pages earlier in the sequence showing more completions than later pages. Using data from a multi-page online survey administered to cohorts recruited on Reddit, this paper analyses the pattern of attrition at various stages of the survey instrument and examines the effects of survey length, time investment, survey format and complexity, and survey delivery on participant attrition. The participant attrition rate (PAR) differed between cohorts, with cohorts drawn from Reddit showing a higher PAR than cohorts targeted by other means. Common to all was that the PAR was higher among younger respondents and among men. Changes in survey question design resulted in the greatest rise in PAR irrespective of age, gender or cohort.

Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study

BACKGROUND

Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to the provision of inappropriate care. Initial testing has revealed that artificially intelligent bots can detect misinformation regarding tanning and essential oils on Reddit dermatology forums and may be able to produce responses to posts containing misinformation.

OBJECTIVE

To analyze the ability of bots to find and respond to tanning and essential oil-related health misinformation on Reddit's dermatology forums in a controlled test environment.

METHODS

Using natural language processing techniques, we trained bots to target misinformation, using relevant keywords and to post prefabricated responses. By evaluating different model architectures across a held-out test set, we compared performances.

RESULTS

Our models yielded data test accuracies ranging 95%-100%, with a Bidirectional Encoder Representations from Transformers (BERT) fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective prefabricated responses to misinformation in a test environment.

CONCLUSIONS

Using a limited data set, bots accurately detected examples of health misinformation within Reddit dermatology forums. Given that these bots can then post prefabricated responses, this technique may allow for interception of misinformation. Providing correct information does not mean that users will be receptive or find such interventions persuasive. Further studies should investigate this strategy's effectiveness to inform future deployment of bots as a technique in combating health misinformation.

Characterizing Discourse about COVID-19 Vaccines: A Reddit Version of the Pandemic Story

It has been one year since the outbreak of the COVID-19 pandemic. The good news is that vaccines developed by several manufacturers are being actively distributed worldwide. However, as more and more vaccines become available to the public, various concerns related to vaccines become the primary barriers that may hinder the public from getting vaccinated. Considering the complexities of these concerns and their potential hazards, this study is aimed at offering a clear understanding about different population groups' underlying concerns when they talk about COVID-19 vaccines-particularly those active on Reddit. The goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative comparisons. Findings include the following: (1) during the pandemic, the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics; (2) each subreddit has its own user bases, so information posted in one subreddit may not reach that from other subreddits; and (3) since users' concerns vary across time and subreddits, communication strategies must be adjusted according to specific needs. The results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs.

Classifying patient and professional voice in social media health posts

Background

Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases).

Results

We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only.

Conclusion

The main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-021-01577-9.

Categorising patient concerns using natural language processing techniques

OBJECTIVES

Patient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.

METHODS

Patient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.

RESULTS

The LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).

DISCUSSION

LDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.

CONCLUSION

Our findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.

Machine Learning Applications in the Evaluation and Management of Psoriasis: A Systematic Review

Background

Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction.

Objective

To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances.

Methods

We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and "psoriasis" in the title and/or abstract.

Results

Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment.

Conclusion

Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.

Artificial Intelligence in Dermatology: A Practical Introduction to a Paradigm Shift

Artificial Intelligence (AI) has surpassed dermatologists in skin cancer detection, but dermatology still lags behind radiology in its broader adoption. Building and using AI applications are becoming increasingly accessible. However, complex use cases may still require specialized expertise for design and deployment. AI has many applications in dermatology ranging from fundamental research, diagnostics, therapeutics, and cosmetic dermatology. The lack of standardization of images and privacy concerns are the foremost challenges stifling AI adoption. Dermatologists have a significant role to play in standardized data collection, curating data for machine learning, clinically validating AI solutions, and ultimately adopting this paradigm shift that is changing the way we practice.

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

Detecting Lung Cancer Trends by Leveraging Real-World and Internet-Based Data: Infodemiology Study

Background

Internet search data on health-related terms can reflect people’s concerns about their health status in near real time, and hence serve as a supplementary metric of disease characteristics. However, studies using internet search data to monitor and predict chronic diseases at a geographically finer state-level scale are sparse.

Objective

The aim of this study was to explore the associations of internet search volumes for lung cancer with published cancer incidence and mortality data in the United States.

Methods

We used Google relative search volumes, which represent the search frequency of specific search terms in Google. We performed cross-sectional analyses of the original and disease metrics at both national and state levels. A smoothed time series of relative search volumes was created to eliminate the effects of irregular changes on the search frequencies and obtain the long-term trends of search volumes for lung cancer at both the national and state levels. We also performed analyses of decomposed Google relative search volume data and disease metrics at the national and state levels.

Results

The monthly trends of lung cancer-related internet hits were consistent with the trends of reported lung cancer rates at the national level. Ohio had the highest frequency for lung cancer-related search terms. At the state level, the relative search volume was significantly correlated with lung cancer incidence rates in 42 states, with correlation coefficients ranging from 0.58 in Virginia to 0.94 in Oregon. Relative search volume was also significantly correlated with mortality in 47 states, with correlation coefficients ranging from 0.58 in Oklahoma to 0.94 in North Carolina. Both the incidence and mortality rates of lung cancer were correlated with decomposed relative search volumes in all states excluding Vermont.

Conclusions

Internet search behaviors could reflect public awareness of lung cancer. Research on internet search behaviors could be a novel and timely approach to monitor and estimate the prevalence, incidence, and mortality rates of a broader range of cancers and even more health issues.

The influence of social media on acne treatment: A cross-sectional survey

BACKGROUND/OBJECTIVES

Social media use has been suggested to worsen psychiatric health among adolescents, especially those with visible skin lesions including acne. However, little is known about social media's impact on acne treatment. The purpose of the study sought to characterize the influence of social media use on acne treatment.

METHODS

We conducted a cross-sectional survey of West Virginia University ambulatory patients whose chief complaint was acne was conducted. The survey collected sociodemographics and queried whether individuals accessed social media for acne treatment advice or not, whether changes to acne care were made based on social media, and whether these changes aligned with the American Academy of Dermatology (AAD) clinical guidelines for acne management.

RESULTS

Of 130 respondents, 45% consulted social media for acne treatment advice (54% of women vs 31% of men). 41% of adolescents and 51% of adults consulted social media. The most used platforms were YouTube and Instagram (58% each). Social media users often tried an OTC treatment (81%) or dietary modification (40%). However, only 31% of participants consulting social media made changes fully aligned with AAD clinical guidelines.

CONCLUSIONS

Social media-influenced acne treatment advice is prevalent, especially among women, adolescents, and young adults. This treatment advice frequently does not align with AAD guidelines, with notably 40% of respondents choosing dietary modification for acne management. These results suggest that dermatologists should inquire about social media acne treatment advice and directly address misinformation.