How do others cope? Extracting coping strategies for adverse drug events from social media

Patients advise their peers on how to cope with their illness in daily life on online support groups. To date, no efforts have been made to automatically extract recommended coping strategies from online patient discussion groups. We introduce this new task, which poses a number of challenges including complex, long entities, a large long-tailed label space, and cross-document relations. We present an initial ontology for coping strategies as a starting point for future research on coping strategies, and the first end-to-end pipeline for extracting coping strategies for side effects. We also compared two possible computational solutions for this novel and highly challenging task; multi-label classification and named entity recognition (NER) with entity linking (EL). We evaluated our methods on the discussion forum from the Facebook group of the worldwide patient support organization 'GIST support international' (GSI); GIST support international donated the data to us. We found that coping strategy extraction is difficult and both methods attain limited performance (measured with F₁ score) on held out test sets; multi-label classification outperforms NER+EL (F₁=0.220 vs F₁=0.155). An inspection of the multi-label classification output revealed that for some of the incorrect predictions, the reference label is close to the predicted label in the ontology (e.g. the predicted label 'juice' instead of the more specific reference label 'grapefruit juice'). Performance increased to F₁=0.498 when we evaluated at a coarser level of the ontology. We conclude that our pipeline can be used in a semi-automatic setting, in interaction with domain experts to discover coping strategies for side effects from a patient forum. For example, we found that patients recommend ginger tea for nausea and magnesium and potassium supplements for cramps. This information can be used as input for patient surveys or clinical studies.

Sample Bias in Web-Based Patient-Generated Health Data of Dutch Patients With Gastrointestinal Stromal Tumor: Survey Study

BACKGROUND

Increasingly, social media is being recognized as a potential resource for patient-generated health data, for example, for pharmacovigilance. Although the representativeness of the web-based patient population is often noted as a concern, studies in this field are limited.

OBJECTIVE

This study aimed to investigate the sample bias of patient-centered social media in Dutch patients with gastrointestinal stromal tumor (GIST).

METHODS

A population-based survey was conducted in the Netherlands among 328 patients with GIST diagnosed 2-13 years ago to investigate their digital communication use with fellow patients. A logistic regression analysis was used to analyze clinical and demographic differences between forum users and nonusers.

RESULTS

Overall, 17.9% (59/328) of survey respondents reported having contact with fellow patients via social media. Moreover, 78% (46/59) of forum users made use of GIST patient forums. We found no statistically significant differences for age, sex, socioeconomic status, and time since diagnosis between forum users (n=46) and nonusers (n=273). Patient forum users did differ significantly in (self-reported) treatment phase from nonusers (P=.001). Of the 46 forum users, only 2 (4%) were cured and not being monitored; 3 (7%) were on adjuvant, curative treatment; 19 (41%) were being monitored after adjuvant treatment; and 22 (48%) were on palliative treatment. In contrast, of the 273 patients who did not use disease-specific forums to communicate with fellow patients, 56 (20.5%) were cured and not being monitored, 31 (11.3%) were on curative treatment, 139 (50.9%) were being monitored after treatment, and 42 (15.3%) were on palliative treatment. The odds of being on a patient forum were 2.8 times as high for a patient who is being monitored compared with a patient that is considered cured. The odds of being on a patient forum were 1.9 times as high for patients who were on curative (adjuvant) treatment and 10 times as high for patients who were in the palliative phase compared with patients who were considered cured. Forum users also reported a lower level of social functioning (84.8 out of 100) than nonusers (93.8 out of 100; P=.008).

CONCLUSIONS

Forum users showed no particular bias on the most important demographic variables of age, sex, socioeconomic status, and time since diagnosis. This may reflect the narrowing digital divide. Overrepresentation and underrepresentation of patients with GIST in different treatment phases on social media should be taken into account when sourcing patient forums for patient-generated health data. A further investigation of the sample bias in other web-based patient populations is warranted.

DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter

Objective

Research on pharmacovigilance from social media data has focused on mining adverse drug events (ADEs) using annotated datasets, with publications generally focusing on 1 of 3 tasks: ADE classification, named entity recognition for identifying the span of ADE mentions, and ADE mention normalization to standardized terminologies. While the common goal of such systems is to detect ADE signals that can be used to inform public policy, it has been impeded largely by limited end-to-end solutions for large-scale analysis of social media reports for different drugs.

Materials and Methods

We present a dataset for training and evaluation of ADE pipelines where the ADE distribution is closer to the average ‘natural balance’ with ADEs present in about 7% of the tweets. The deep learning architecture involves an ADE extraction pipeline with individual components for all 3 tasks.

Results

The system presented achieved state-of-the-art performance on comparable datasets and scored a classification performance of F₁ = 0.63, span extraction performance of F₁ = 0.44 and an end-to-end entity resolution performance of F₁ = 0.34 on the presented dataset.

Discussion

The performance of the models continues to highlight multiple challenges when deploying pharmacovigilance systems that use social media data. We discuss the implications of such models in the downstream tasks of signal detection and suggest future enhancements.

Conclusion

Mining ADEs from Twitter posts using a pipeline architecture requires the different components to be trained and tuned based on input data imbalance in order to ensure optimal performance on the end-to-end resolution task.

Patients with rare endocrine conditions have corresponding views on unmet needs in clinical research

PURPOSE

European Reference Network on Rare Endocrine Conditions' (Endo-ERN) mission is to reduce and ultimately abolish inequalities in care for patients with rare endocrine conditions in Europe. This study assesses which themes related to rare endocrine conditions are prioritized by patients for clinical research.

METHODS

A survey was developed, translated into 22 different European languages, and distributed to patients with rare endocrine conditions. Patients were asked to give priority scores to listed prespecified topics: fertility, heritability, tiredness, daily medicine intake, sleep quality, physical discomfort, and ability to work, partake in social life, and sports. They were also asked to suggest further important areas for research in open fields.

RESULTS

After data cleaning, 1378 survey responses were analyzed. Most responses were received from Northern (47%) and Western Europeans (39%), while Southern (11%) and Eastern Europe (2%) were underrepresented. Respondents were most interested in research concerning ability to participate in social life and work. Patients suggested key areas to work: long-term side effects of medical treatments and quality of life. Some priorities differed between disease groups, both for prespecified and open topics and reflected aspects of patients' individual conditions.

CONCLUSIONS

With this large survey, Endo-ERN gained insight into patients' unmet needs in scientific research. Patients prioritized research on ability to work and participation in social activities, though needs differ between the disease groups. Clinical experts should incorporate the results of this survey into the design of future studies on rare endocrine conditions. We aim to utilize these results in designing patient-reported outcome measures for the disease areas covered by Endo-ERN.

An exploratory study of COVID-19 misinformation on Twitter

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1500 tweets relating to 1274 false and 226 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating(new tweets) or spreading(retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.

ERPs Reveal Disengagement Processes Related to Condom Use Embarrassment in Intention-Behavior Inconsistent Young Adults

The use of barrier protections such as condoms has consistently been reported to reduce the acquisition of sexually transmitted infections. However, it has also been reported that the association between condom use intentions and behavior is, at best, often weak. Furthermore, embarrassment associated with purchasing condoms and negotiating their use has been shown to negatively impact the frequency of condom use. Using electroencephalography to analyze P300 event-related potential components known to measure early attention allocation, we examined electrophysiological evidence of early attention disengagement for embarrassing health information. Forty young adults-34 females and six males-participated in an adapted version of Posner's visual cueing paradigm. All were high in intention to use condoms, but half were intention-behavior consistent and half were intention-behavior inconsistent. Compared to intention-behavior consistent participants, those with intention-behavior inconsistency showed a reduced P300 component when attending to a visual target opposite to the field in which embarrassing self-relevant health information was presented, indicating more efficient early attention disengagement from such embarrassing health information. In conclusion, our electrophysiological data suggest that high intention alone may be not sufficient to predict adolescents' condom use behavior.