Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations

Patient Information Needs and Decision-Making Before a Cardiac Implantable Electronic Device: A Qualitative Study Utilizing Social Media Data

The decision to receive a cardiac implantable electronic device (CIED) represents a challenging experience for patients. However, the majority of previous research has only considered retrospective accounts of patient experiences. This study aimed to use social media data to characterize the information sought by people anticipating or considering CIED implantation and factors that influence their decision-making experiences. A Python-based script was used to collect posts made to a community intended for discussions concerning CIEDs on the social media platform Reddit. Reflexive content analysis was used to analyze the collected data. From 799 posts collected, 101 made by 86 participants were analyzed. The reported median (range) age of participants was 34 (16-67), and most were anticipating or considering a pacemaker. Three overarching categories classified the data: "Use of social media to meet informational and other needs"; "Factors influencing acceptance of the need for implantation"; and "Specific concerns considered during decision-making." Participants anticipating or considering a CIED predominantly sought experiential information. Among asymptomatic participants, doubts were prevalent, with acceptance being an influential factor in decision-making. Healthcare professionals should recognize the informational and emotional needs of prospective CIED patients and tailor support mechanisms to better facilitate their decision-making.

A text analytics approach for mining public discussions in online cancer forum: Analysis of multi-intent lung cancer treatment dataset

BACKGROUND

Online cancer forums (OCF) are increasingly popular platforms for patients and caregivers to discuss, seek information on, and share opinions about diseases and treatments. This interaction generates a substantial amount of unstructured text data, necessitating deeper exploration. Using time series data, our study exploits topic modeling in the novel domain of online cancer forums (OCFs) to identify meaningful topics and changing dynamics of online discussion across different lung cancer treatment intent groups.

METHODS

For this purpose, a dataset comprising 27,998 forum posts about lung cancer was collected from three OCFs: lungcancer.net, lungevity.org, and reddit.com, spanning the years 2016 to 2018.

RESULTS

The analysis reflects the public discussion on multi-intent lung cancer treatment over time, taking into account seasonal variations. Discussions on cancer symptoms and prevention garnered the most attention, dominating both curative and palliative care discussions. There were distinct seasonal peaks: curative care topics surged from winter to late spring, while palliative care topics peaked from late summer to mid-autumn. Keyword analysis highlighted that lung cancer diagnosis and treatment were primary topics, whereas cancer prevention and treatment outcomes were predominant across multi-care contexts. For the study period, curative care discussions predominantly revolved around informational support and disease syndromes. In contrast, social support and cancer prevention prevailed in the palliative care context. Notably, topics such as cancer screening and cancer treatment exhibit pronounced seasonal variations in curative care, peaking in frequency during the summers (May to August) of the study period. Meanwhile, the topic of tumor control within palliative care showed significant seasonal influence during the winters and summers of 2017 and 2018.

CONCLUSION

Our text analysis approach using OCF data shows potential for computational methods in this novel domain to gain insights into trends in public cancer communication and seasonal variations for a better understanding of improving personalized care, decision support, treatment outcomes, and quality of life.

Identifying mental health discussion topic in social media community: subreddit of bipolar disorder analysis

Online platforms allow individuals to connect with others, share experiences, and find communities with similar interests, providing a sense of belonging and reducing feelings of isolation. Numerous previous studies examined the content of online health communities to gain insights into the sentiments surrounding mental health conditions. However, there is a noticeable gap in the research landscape, as no study has specifically concentrated on conducting an in-depth analysis or providing a comprehensive visualization of Bipolar disorder. Therefore, this study aimed to address this gap by examining the Bipolar subreddit online community, where we collected 1,460,447 posts as plain text documents for analysis. By employing LDA topic modeling and sentiment analysis, we found that the Bipolar disorder online community on Reddit discussed various aspects of the condition, including symptoms, mood swings, diagnosis, and medication. Users shared personal experiences, challenges, and coping strategies, seeking support and connection. Discussions related to therapy and medication were prevalent, emphasizing the importance of finding suitable therapists and managing medication side effects. The online community serves as a platform for seeking help, advice, and information, highlighting the role of social support in managing bipolar disorder. This study enhances our understanding of individuals living with bipolar disorder and provides valuable insights and feedback for researchers developing mental health interventions.

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.

The Long COVID experience from a patient's perspective: a clustering analysis of 27,216 Reddit posts

Objective

This work aims to study the profiles of Long COVID from the perspective of the patients spontaneously sharing their experiences and symptoms on Reddit.

Methods

We collected 27,216 posts shared between July 2020 and July 2022 on Long COVID-related Reddit forums. Natural language processing, clustering techniques and a Long COVID symptoms lexicon were used to extract the different symptoms and categories of symptoms and to study the co-occurrences and correlation between them.

Results

More than 78% of the posts mentioned at least one Long COVID symptom. Fatigue (29.4%), pain (22%), clouded consciousness (19.1%), anxiety (17.7%) and headaches (15.6%) were the most prevalent symptoms. They also highly co-occurred with a variety of other symptoms (e.g., fever, sinonasal congestion). Different categories of symptoms were found: general (45.5%), neurological/ocular (42.9%), mental health/psychological/behavioral (35.2%), body pain/mobility (35.1%) and cardiorespiratory (31.2%). Posts focusing on other concerns of the community such as vaccine, recovery and relapse and, symptom triggers were detected.

Conclusions

We demonstrated the benefits of leveraging large volumes of data from Reddit to characterize the heterogeneity of Long COVID profiles. General symptoms, particularly fatigue, have been reported to be the most prevalent and frequently co-occurred with other symptoms. Other concerns, such as vaccination and relapse following recovery, were also addressed by the Long COVID community.

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.

The Disruption of the Cystic Fibrosis Community’s Experiences and Concerns during the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments (Preprint)

BACKGROUND

The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods.

OBJECTIVE

This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community's experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases.

METHODS

We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of "1" was assigned to months in 2020 and "0" otherwise and tested for its statistical significance.

RESULTS

A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community's experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period.

CONCLUSIONS

There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.

Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia

Background

Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging.

Objective

The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit.

Methods

We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19–related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country.

Results

Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19–related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits.

Conclusions

This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.

Medicalizing risk: How experts and consumers manage uncertainty in genetic health testing

Given increased prevalence of direct-to-consumer (DTC) genetic health tests in recent years, this paper delves into discourses among researchers at professional genomics conferences and lay DTC genetic test users on popular discussion website Reddit to understand the contested value of genetic knowledge and its direct implications for health management. Harnessing ethnographic observations at five conferences and a text -analysis of 52 Reddit threads, we find both experts and lay patient-consumers navigate their own versions of “productive uncertainty.” Experts develop genetic technologies to legitimize unsettled genomics as medical knowledge and mobilize resources and products, while lay patient-consumers turn to Internet forums to gain clarity on knowledge gaps that help better manage their genetic risk states. By showing how the uncertain nature of genomics serves as a productive force placing both parties within a mutually cooperative cycle, we argue that experts and patient-consumers co-produce a form of relational medicalization that concretizes “risk” itself as a disease state.

Donating Health Data to Research: Influential Characteristics of Individuals Engaging in Self-Tracking

Health self-tracking is an ongoing trend as software and hardware evolve, making the collection of personal data not only fun for users but also increasingly interesting for public health research. In a quantitative approach we studied German health self-trackers (N = 919) for differences in their data disclosure behavior by comparing data showing and sharing behavior among peers and their willingness to donate data to research. In addition, we examined user characteristics that may positively influence willingness to make the self-tracked data available to research and propose a framework for structuring research related to self-measurement. Results show that users’ willingness to disclose data as a “donation” more than doubled compared to their “sharing” behavior (willingness to donate = 4.5/10; sharing frequency = 2.09/10). Younger men (up to 34 years), who record their vital signs daily, are less concerned about privacy, regularly donate money, and share their data with third parties because they want to receive feedback, are most likely to donate data to research and are thus a promising target audience for health data donation appeals. The paper adds to qualitative accounts of self-tracking but also engages with discussions around data sharing and privacy.

Let's Talk About It: A Narrative Review of Digital Approaches for Disseminating and Communicating Health Research and Innovation

Best health practice and policy are derived from research, yet the adoption of research findings into health practice and policy continues to lag. Efforts to close this knowledge-to-action gap can be addressed through knowledge translation, which is composed of knowledge synthesis, dissemination, exchange, and application. Although all components warrant investigation, improvements in knowledge dissemination are particularly needed. Specifically, as society continues to evolve and technology becomes increasingly present in everyday life, knowing how to share research findings (with the appropriate audience, using tailored messaging, and through the right digital medium) is an important component towards improved health knowledge translation. As such, this article presents a review of digital presentation formats and communication channels that can be leveraged by health researchers, as well as practitioners and policy makers, for knowledge dissemination of health research. In addition, this article highlights a series of additional factors worth consideration, as well as areas for future direction.

Harnessing the Power of Social Media to Understand the Impact of COVID-19 on People Who Use Drugs During Lockdown and Social Distancing

OBJECTIVES

This paper uses a social media platform, Reddit, to identify real-time experiences of people who use drugs during the COVID-19 lock-down.

METHODS

Reddit is a popular and growing social media platform, providing a large, publicly available dataset necessary for high performance of machine learning and topic modeling techniques. We used opioid-related "subreddits," communities where Reddit users engage in conversations about drug use, to examine COVID-19-related content of posts and comments from March to May 2020. This paper investigates the latent topics of users' posts/comments using Latent Dirichlet Allocation, an unsupervised machine learning approach that uncovers the thematic structure of a document collection. We also examine how topics changed over time.

RESULTS

The final dataset consists of 525 posts and 9284 comments, for a total of 9809 posts/comments (3756 posts/comments in r/opiates, 1641 in r/OpiatesRecovery, 1203 in r/suboxone, and 3209 in r/Methadone) among 2342 unique individuals. There were 5256 posts/comments in March; 3185 in April; and 1368 in May (until May 22). Topics that appeared most frequently in COVID-19-related discussions included medication for opioid use disorder experiences and access issues (22.6%), recovery (24.2%), and drug withdrawal (20.2%).

CONCLUSIONS

During the first three months of the COVID-19 pandemic, people who use drugs were impacted in several ways, including forced or intentional withdrawal, confusion between withdrawal and COVID-19 symptoms, take-home medication for opioid use disorder issues, and barriers to recovery. As the pandemic progresses, providers and policymakers should consider these experiences among people who use drugs during the early stage of the pandemic.

Development of a Lexicon for Pain

Pain has been an area of growing interest in the past decade and is known to be associated with mental health issues. Due to the ambiguous nature of how pain is described in text, it presents a unique natural language processing (NLP) challenge. Understanding how pain is described in text and utilizing this knowledge to improve NLP tasks would be of substantial clinical importance. Not much work has previously been done in this space. For this reason, and in order to develop an English lexicon for use in NLP applications, an exploration of pain concepts within free text was conducted. The exploratory text sources included two hospital databases, a social media platform (Twitter), and an online community (Reddit). This exploration helped select appropriate sources and inform the construction of a pain lexicon. The terms within the final lexicon were derived from three sources—literature, ontologies, and word embedding models. This lexicon was validated by two clinicians as well as compared to an existing 26-term pain sub-ontology and MeSH (Medical Subject Headings) terms. The final validated lexicon consists of 382 terms and will be used in downstream NLP tasks by helping select appropriate pain-related documents from electronic health record (EHR) databases, as well as pre-annotating these words to help in development of an NLP application for classification of mentions of pain within the documents. The lexicon and the code used to generate the embedding models have been made publicly available.

A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness

Background

A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature.

Objective

The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII.

Methods

We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites.

Results

Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants.

Conclusions

Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses.

Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing

INTRODUCTION

The relationship between cannabis, tobacco, and vaping devices is both rapidly changing and poorly understood, with consumers rapidly shifting between use of all three product types. Given this dynamic and evolving landscape, there is an urgent need to monitor and better understand co-use, dual-use, and transition patterns between these products. This study describes work that utilizes social media - in this case, Reddit - in conjunction with automated Natural Language Processing (NLP) methods to better understand cannabis, tobacco, and vaping device product usage patterns.

METHODS

We collected Reddit data from the period 2013-2018, sourced from eight popular, high-volume Reddit communities (subreddits) related to the three product categories. We then manually annotated (coded) a set of 2640 Reddit posts and trained a machine learning-based NLP algorithm to automatically identify and disambiguate between cannabis or tobacco mentions (both smoking and vaping) in Reddit posts. This classifier was then applied to all data derived from the eight subreddits, 767,788 posts in total.

RESULTS

The NLP algorithm achieved an overall moderate performance (overall F-score of 0.77). When applied to our large corpus of Reddit posts, we discovered that over 10% of posts in the smoking cessation subreddit r/stopsmoking were classified as referring to vaping nicotine, and that only 2% of posts from the subreddits r/electronic_cigarette and r/vaping were classified as referring to smoking (tobacco) cessation.

CONCLUSIONS

This study presents the results of applying an NLP algorithm designed to identify and distinguish between cannabis and tobacco mentions (both smoking and vaping) in Reddit posts, hence contributing to our currently limited understanding of co-use, dual-use, and transition patterns between these products.

The role of social media for patients with temporomandibular disorders: A content analysis of Reddit

BACKGROUND

Social media is frequently used to discuss health topics among users. Reddit is a popular social media platform particularly suit for discussion about chronic illness because of its anonymity that allow users to express uninhibited feelings. Temporomandibular disorders (TMD) represent a chronic painful disorder which has been rarely studied in terms of social media discussion.

OBJECTIVES

By exploring how Reddit is used to discuss about TMD, we aim to raise awareness to clinicians involved in TMD management about the online discussion on this topic.

METHODS

A quantitative content analysis was performed on a pool of most relevant threads and comments about the topic "TMJ" on Reddit. Following a codebook, two independent coders assessed multiple clinically relevant variables. A third subject resolved eventual discrepancies.

RESULTS

Reddit is mostly used by subjects with TMD asking for advice to other users about symptoms and treatment modalities. The most discussed causes of TMD were bruxism and dental occlusion, and the most discussed treatments were oral appliance therapy and complementary and alternative treatments. The most favourable opinions about treatment modalities were for self-care and behavioural therapy while the least favourable opinions were for surgery and irreversible dental treatments.

CONCLUSIONS

Reddit represents an excellent data-mining platform to retrieve valuable information about health-related discussion by the community. Our findings suggest an overall alignment of such discussion with evidence-based science about TMD; however, to further increase this trend, we encourage healthcare provider to take an active role in the digital spread of scientifically valid information.

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

BACKGROUND

The World Health Organization's International Classification of Functioning Disability and Health (ICF) conceptualizes disability not solely as a problem that resides in the individual, but as a health experience that occurs in a context. Word embeddings build on the idea that words that occur in similar contexts tend to have similar meanings. In spite of both sharing "context" as a key component, word embeddings have been scarcely applied in disability. In this work, we propose social media (particularly, Reddit) to link them.

OBJECTIVE

The objective of our study is to train a model for generating word associations using a small dataset (a subreddit on disability) able to retrieve meaningful content. This content will be formally validated and applied to the discovery of related terms in the corpus of the disability subreddit that represent the physical, social, and attitudinal environment (as defined by a formal framework like the ICF) of people with disabilities.

METHODS

Reddit data were collected from pushshift.io with the pushshiftr R package as a wrapper. A word2vec model was trained with the wordVectors R package using the disability subreddit comments, and a preliminary validation was performed using a subset of Mikolov analogies. We used Van Overschelde's updated and expanded version of the Battig and Montague norms to perform a semantic categories test. Silhouette coefficients were calculated using cosine distance from the wordVectors R package. For each of the 5 ICF environmental factors (EF), we selected representative subcategories addressing different aspects of daily living (ADLs); then, for each subcategory, we identified specific terms extracted from their formal ICF definition and ran the word2vec model to generate their nearest semantic terms, validating the obtained nearest semantic terms using public evidence. Finally, we applied the model to a specific subcategory of an EF involved in a relevant use case in the field of rehabilitation.

RESULTS

We analyzed 96,314 comments posted between February 2009 and December 2019, by 10,411 Redditors. We trained word2vec and identified more than 30 analogies (eg, breakfast - 8 am + 8 pm = dinner). The semantic categorization test showed promising results over 60 categories; for example, s(A relative)=0.562, s(A sport)=0.475 provided remarkable explanations for low s values. We mapped the representative subcategories of all EF chapters and obtained the closest terms for each, which we confirmed with publications. This allowed immediate access (≤ 2 seconds) to the terms related to ADLs, ranging from apps "to know accessibility before you go" to adapted sports (boccia). For example, for the support and relationships EF subcategory, the closest term discovered by our model was "resilience," recently regarded as a key feature of rehabilitation, not yet having one unified definition. Our model discovered 10 closest terms, which we validated with publications, contributing to the "resilience" definition.

CONCLUSIONS

This study opens up interesting opportunities for the exploration and discovery of the use of a word2vec model that has been trained with a small disability dataset, leading to immediate, accurate, and often unknown (for authors, in many cases) terms related to ADLs within the ICF framework.

Discussing non-invasive prenatal testing on Reddit: The benefits, the concerns, and the comradery

OBJECTIVE

As the use of non-invasive prenatal testing (NIPT) increases, its benefits and concerns are being examined through surveys, qualitative studies, and bioethical analysis. However, only scant research has examined public discourse on the topic. This research examined NIPT discussions on the social media platform Reddit.

METHOD

Content and qualitative description analysis was performed on 98 NIPT discussions (2682 comments), obtained by inputting "NIPT" into Reddit's search engine.

RESULTS

Detailing of benefits and concerns was found in collaborative and supportive discussions. Overall, NIPT is seen as valuable and desirable. Some concerns focused on cost-related barriers to access, anxiety related to testing, and interpretation of results. NIPT is often portrayed as offering peace of mind and is sometimes described as a means of preparing for possible outcomes.

CONCLUSION

In the discussions analyzed, NIPT is seen, overall, as valuable and greater access to it is desired. Some questions and concerns about NIPT were evident. Reddit stands as a valuable and appreciated tool for individuals wishing to discuss NIPT and to solicit and share information, opinions, and experiences. Health care providers should consider the ways social platforms such as Reddit can be engaged to better inform and educate the public.

"Beyond the Bump" - Insight Into the Postpartum Women's Experience of Pelvic Organ Prolapse as Expressed on Reddit

OBJECTIVE

To qualitatively analyze the biopsychological experiences of postpartum women regarding pelvic organ prolapse (POP) as expressed on Reddit, a widely used internet platform for anonymous discussion and information sharing. POP is a prevalent condition in postpartum women, yet personal experiences are often not discussed due to social stigma.

METHODS

"Beyondthebump," a Subreddit forum for postpartum mothers with >57,000 subscribers, was queried for "prolapse" to collect postings on POP in August 2018. Posts were analyzed qualitatively by 2 independent researchers. The principles of grounded theory were applied and preliminary themes were generated. These themes were used to derive emergent concepts.

RESULTS

We analyzed 28 unique posts with 390 responses from 2014 to 2018. Qualitative analysis yielded 3 preliminary themes. (1) Women were unaware POP could occur postpartum and frustrated by the lack of prenatal discussion. (2) Women expressed a need for supportive, comprehensive, and immediate care. (3) Attributed symptoms of POP included pain and discomfort, causing difficulty with daily life. Three emergent concepts were derived. (1) POP is a difficult, debilitating condition with mental and physical effects. (2) Women with POP were self-driven and actively sought help. (3) There was motivation to increase POP awareness.

CONCLUSION

Postpartum women's perspectives on POP focused on the difficulty of continuing life routines, self-drive for improvement, and motivation to raise awareness for others. Through learning from women's self-reported experiences, physicians can better meet women's needs and improve care for POP.

Topic Modeling for Analyzing Patients' Perceptions and Concerns of Hearing Loss on Social Q&A Sites: Incorporating Patients' Perspective

Hearing loss is the most common human sensory deficit, affecting normal communication. Recently, patients with hearing loss or at risk of hearing loss are increasingly turning to the online health community for health information and support. Information on health-related topics exchanged on the Internet is a useful resource to examine patients' informational needs. The ability to understand the patients' perspectives on hearing loss is critical for health professionals to develop a patient-centered intervention. In this paper, we apply Latent Dirichlet Allocation (LDA) on electronic patient-authored questions on social question-and-answer (Q&A) sites to identify patients' perceptions, concerns, and needs on hearing loss. Our results reveal 21 topics, which are both representative and meaningful, and mostly correspond to sub-fields established in hearing science research. The latent topics are classified into five themes, which include "sudden hearing loss", "tinnitus", "noise-induced hearing loss", "hearing aids", "dizziness", "curiosity about hearing loss", "otitis media" and "complications of disease". Our topic analysis of patients' questions on the topic of hearing loss allows achieving a thorough understanding of patients' perspectives, thereby leading to better development of the patient-centered intervention.

Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed

BACKGROUND

Natural language processing (NLP) is an important traditional field in computer science, but its application in medical research has faced many challenges. With the extensive digitalization of medical information globally and increasing importance of understanding and mining big data in the medical field, NLP is becoming more crucial.

OBJECTIVE

The goal of the research was to perform a systematic review on the use of NLP in medical research with the aim of understanding the global progress on NLP research outcomes, content, methods, and study groups involved.

METHODS

A systematic review was conducted using the PubMed database as a search platform. All published studies on the application of NLP in medicine (except biomedicine) during the 20 years between 1999 and 2018 were retrieved. The data obtained from these published studies were cleaned and structured. Excel (Microsoft Corp) and VOSviewer (Nees Jan van Eck and Ludo Waltman) were used to perform bibliometric analysis of publication trends, author orders, countries, institutions, collaboration relationships, research hot spots, diseases studied, and research methods.

RESULTS

A total of 3498 articles were obtained during initial screening, and 2336 articles were found to meet the study criteria after manual screening. The number of publications increased every year, with a significant growth after 2012 (number of publications ranged from 148 to a maximum of 302 annually). The United States has occupied the leading position since the inception of the field, with the largest number of articles published. The United States contributed to 63.01% (1472/2336) of all publications, followed by France (5.44%, 127/2336) and the United Kingdom (3.51%, 82/2336). The author with the largest number of articles published was Hongfang Liu (70), while Stéphane Meystre (17) and Hua Xu (33) published the largest number of articles as the first and corresponding authors. Among the first author's affiliation institution, Columbia University published the largest number of articles, accounting for 4.54% (106/2336) of the total. Specifically, approximately one-fifth (17.68%, 413/2336) of the articles involved research on specific diseases, and the subject areas primarily focused on mental illness (16.46%, 68/413), breast cancer (5.81%, 24/413), and pneumonia (4.12%, 17/413).

CONCLUSIONS

NLP is in a period of robust development in the medical field, with an average of approximately 100 publications annually. Electronic medical records were the most used research materials, but social media such as Twitter have become important research materials since 2015. Cancer (24.94%, 103/413) was the most common subject area in NLP-assisted medical research on diseases, with breast cancers (23.30%, 24/103) and lung cancers (14.56%, 15/103) accounting for the highest proportions of studies. Columbia University and the talents trained therein were the most active and prolific research forces on NLP in the medical field.

Characterizing Pathways of Non-oral Prescription Stimulant Non-medical Use Among Adults Recruited From Reddit

Objective: Prescription stimulant non-medical use (NMU) is a national predicament. While the risks of prescription stimulant NMU have been considered, less is known about non-oral use. To focus on this gap, a sample of adults with non-oral prescription stimulant NMU within the last 5-years was recruited. The purpose of the present study was to characterize the pathways and substance transitions associated with prescription stimulant NMU and non-oral prescription stimulant NMU in this unique sample of adults. Methods: Adults (n = 225) reporting non-oral prescription stimulant NMU within the last 5 years were recruited to complete an online survey by banner ads placed on the Reddit website between February and September 2019. After completion of the survey, a second study consisting of an in-depth telephone interview was conducted with 23 participants: interviews took place between July and September 2019. Data reported here include substance, route of administration and class transitions, as well as qualitative data from the interviews. Results: Approximately 1 in 5 began their substance use trajectory with prescription stimulants (19.1%). Other than marijuana, most exposures to illicit substances occurred after both initial prescription stimulant NMU and initial non-oral prescription stimulant NMU. The most frequently reported route of administration transition was from oral use to snorting (n = 158, 70.2%), however, other route of administration transitions included oral use to injection drug use (n = 14, 6%). In-depth interviews elaborated upon these transitions and indicated that prescription stimulant NMU was consequential to substance use pathways. Conclusions: Oral prescription stimulant NMU was a precursor to non-oral prescription stimulant NMU. Non-oral prescription stimulant NMU was a precursor to illicit substance use, suggesting that prescription stimulant NMU impacts substance use pathways and revealing opportunities for intervention.