What are IBD Patients Talking About on Twitter? Using Natural Language Understanding to Investigate Patients' Tweets.

This research aims to investigate what patients with inflammatory bowel disease (IBD) are talking about on Twitter and learn from the experimental knowledge they share online. The study presents a framework for analyzing patients' tweets and comparing their content to tweets of the general population. We started by constructing two datasets of tweets-a dataset of patients' tweets and a control dataset for comparison. Then, we thematically classified the tweets and obtained a subset of tweets related to health and nutrition. We used a Dirichlet regression to compare the thematic segmentations of the two groups. We continued by extracting keywords from the filtered tweets and applying entity sentiment analysis to determine the patients' sentiments towards the extracted keywords. Finally, we detected emotions within the tweets and used a Wilcoxon test to compare the emotions conveyed in each group. We found statistically significant differences between the patients' thematic segmentations and those of the control group and observed significant differences in the emotions each group expressed while talking about health. Not only do patients talk more about health in comparison to the general Twitter population, but they also address the subject with negative sentiments and express more negative emotions. The personal information IBD patients share on Twitter can be used to derive complementary knowledge about the disease and provide an additional foundation to existing medical research on IBD. The four stages of the study are also feasible to extend to other chronic conditions.

Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study.

BACKGROUND: Patients use social media as an alternative information source, where they share information and provide social support. Although large amounts of health-related data are posted on Twitter and other social networking platforms each day, research using social media data to understand chronic conditions and patients' lifestyles is limited. OBJECTIVE: In this study, we contributed to closing this gap by providing a framework for identifying patients with inflammatory bowel disease (IBD) on Twitter and learning from their personal experiences. We enabled the analysis of patients' tweets by building a classifier of Twitter users that distinguishes patients from other entities. This study aimed to uncover the potential of using Twitter data to promote the well-being of patients with IBD by relying on the wisdom of the crowd to identify healthy lifestyles. We sought to leverage posts describing patients' daily activities and their influence on their well-being to characterize lifestyle-related treatments. METHODS: In the first stage of the study, a machine learning method combining social network analysis and natural language processing was used to automatically classify users as patients or not. We considered 3 types of features: the user's behavior on Twitter, the content of the user's tweets, and the social structure of the user's network. We compared the performances of several classification algorithms within 2 classification approaches. One classified each tweet and deduced the user's class from their tweet-level classification. The other aggregated tweet-level features to user-level features and classified the users themselves. Different classification algorithms were examined and compared using 4 measures: precision, recall, F1 score, and the area under the receiver operating characteristic curve. In the second stage, a classifier from the first stage was used to collect patients' tweets describing the different lifestyles patients adopt to deal with their disease. Using IBM Watson Service for entity sentiment analysis, we calculated the average sentiment of 420 lifestyle-related words that patients with IBD use when describing their daily routine. RESULTS: Both classification approaches showed promising results. Although the precision rates were slightly higher for the tweet-level approach, the recall and area under the receiver operating characteristic curve of the user-level approach were significantly better. Sentiment analysis of tweets written by patients with IBD identified frequently mentioned lifestyles and their influence on patients' well-being. The findings reinforced what is known about suitable nutrition for IBD as several foods known to cause inflammation were pointed out in negative sentiment, whereas relaxing activities and anti-inflammatory foods surfaced in a positive context. CONCLUSIONS: This study suggests a pipeline for identifying patients with IBD on Twitter and collecting their tweets to analyze the experimental knowledge they share. These methods can be adapted to other diseases and enhance medical research on chronic conditions.

Identification and Characterization of Human Observational Studies in Nutritional Epidemiology on Gut Microbiomics for Joint Data Analysis.

In any research field, data access and data integration are major challenges that even large, well-established consortia face. Although data sharing initiatives are increasing, joint data analyses on nutrition and microbiomics in health and disease are still scarce. We aimed to identify observational studies with data on nutrition and gut microbiome composition from the Intestinal Microbiomics (INTIMIC) Knowledge Platform following the findable, accessible, interoperable, and reusable (FAIR) principles. An adapted template from the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) consortium was used to collect microbiome-specific information and other related factors. In total, 23 studies (17 longitudinal and 6 cross-sectional) were identified from Italy (7), Germany (6), Netherlands (3), Spain (2), Belgium (1), and France (1) or multiple countries (3). Of these, 21 studies collected information on both dietary intake (24 h dietary recall, food frequency questionnaire (FFQ), or Food Records) and gut microbiome. All studies collected stool samples. The most often used sequencing platform was Illumina MiSeq, and the preferred hypervariable regions of the 16S rRNA gene were V3-V4 or V4. The combination of datasets will allow for sufficiently powered investigations to increase the knowledge and understanding of the relationship between food and gut microbiome in health and disease.