Maya Stemmer’s research while affiliated with Ben-Gurion University of the Negev and other places

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Publications (8)


Natural Language Processing for Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience
  • Article

January 2024

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1 Citation

Procedia Computer Science

Maya Stemmer

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An illustration of the first stage of the study—collecting two datasets of tweets, a main dataset containing patients’ tweets and a control dataset containing tweets of random Twitter users. The patients’ group was obtained in an earlier study. The control group was obtained by sampling Twitter Stream API over different time periods and randomly collecting private accounts. Then, we used Twitter Search API to collect the timelines of the users and obtained two datasets of tweets
An illustration of the second, third, and fourth stages of the study—classifying the tweets into categories and focusing on tweets related to health and nutrition; extracting keywords from patients’ tweets and determining the patients’ sentiment toward them; and detecting emotions within the tweets. After each stage, we perform statistical analyses to compare between the patients’ group and the control group and learn on patients’ behavior
Predicted probability of each category by group: the patients’ group is marked with green circles and the control group with red diamonds. The patients talked more about health and fitness and food and drink. In contrast, the control group talked more about society and politics
Scaled differences between the predicted values of the patients’ group and the predicted values of the control in each category. Green lines, indicating positive differences, signal that the patients talked more about the respective subjects. Red lines, indicating negative differences, signal that the patients talked less about the respective topics. Substantial differences can be identified in favor of the patients in the “health and fitness” and “food and drink” categories
Word clouds of frequently mentioned keywords within patients’ tweets by four groups: all tweets combined, tweets related to health and fitness, tweets related to food and drink, and tweets related to IBD. The keywords are colored based on their sentiments—green representing positive sentiment and red representing negative sentiment. The three clouds containing health-related keywords show a more negative sentiment than the one related to food and drink

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What are IBD Patients Talking About on Twitter? Using Natural Language Understanding to Investigate Patients’ Tweets
  • Article
  • Publisher preview available

April 2023

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44 Reads

SN Computer Science

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.

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Characteristics of the identified observational studies within the KP-INTIMIC consortium.
Cont.
Microbiome data available in the observational studies participating in the KP-INTIMIC consortium.
Gut microbiome assessment in stool samples from the observational studies participating in the KP-INTIMIC consortium.
Identification and Characterization of Human Observational Studies in Nutritional Epidemiology on Gut Microbiomics for Joint Data Analysis Characterization of Human Observational Studies in Nutritional Epidemiology on Gut Microbiomics for Joint Data Analysis. Nutrients

September 2021

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217 Reads

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6 Citations

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.


Identifying Patients on Twitter and Learning from Their Personal Experience: The Case of IBD (Preprint)

March 2021

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34 Reads

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3 Citations

Journal of Medical Internet Research

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.


Identifying Patients on Twitter and Learning from Their Personal Experience: The Case of IBD (Preprint)

March 2021

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25 Reads

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1 Citation

BACKGROUND Social media serve as an alternate information source for patients, who use them to share information and provide social support. Though large amounts of health-related data are being posted on Twitter and other social networking platforms each day, research using social media data for understanding chronic conditions and patients' lifestyles is still lacking. OBJECTIVE In this research we contribute to closing this gap by providing a framework for identifying patients with Inflammatory Bowel Disease (IBD) on Twitter and learning from their personal experience. We enable the analysis of patients' tweets by building a classifier of Twitter users that distinguishes patients from other entities. The research aims to assess the feasibility of using social media data to promote chronically ill patients' wellbeing, by relying on the wisdom of the crowd for identifying healthy lifestyles. We seek to leverage posts describing patients' daily activities and the influence on their wellbeing for characterizing different treatments and understanding what works for whom. METHODS In the first stage of the research, a machine learning method combining both social network analysis and natural language processing was used to classify users as patients or not automatically. Three types of features were considered: (1) the user's behavior on Twitter, (2) the content of the user's tweets, and (3) the social structure of the user's network. Different classification algorithms were examined and compared using two measures (F1-score and precision) over 10-fold cross-validation. In the second stage of the research, the obtained classification methods were used to collect tweets of patients, in which they refer to the different lifestyle changes they endure in order to deal with their disease. Using IBM Watson Service for entity sentiment analysis, we calculated the average sentiment of 420 lifestyle-related words that IBD patients use when describing their daily routine. RESULTS The best classification results (F1-score 0.808 and precision 0.809) for identifying IBD patients among Twitter users were achieved by a multiple-instance learning approach, which constitutes the novelty of this research. The sentiment analysis of tweets written by IBD patients identified frequently mentioned lifestyles and their influence on patients' wellbeing. The findings reinforced what is known about suitable nutrition for IBD, and several foods that are known to cause inflammation were highlighted as words with negative sentiment. CONCLUSIONS Patients everywhere use social media to share health and treatment information, learn from each other's experiences, and provide social support. Mining these informative conversations may shed some light on patients' ways of life and support chronic conditions research.


What Are IBD Patients Talking About on Twitter?

January 2021

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4 Reads

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16 Citations

Communications in Computer and Information Science

In recent years, social networking sites and online communities have served as alternate information sources for patients, who use social media to share health and treatment information, learn from each other’s experiences, and provide social support. This research aimed to investigate what patients with Inflammatory Bowel Disease (IBD) are talking about on Twitter and to learn from the experimental knowledge of living with the disease they share online. We collected tweets of 337 IBD patients who openly tweeted about their disease on Twitter and used the Natural Language Understanding (NLU) module by IBM Cloud to apply category classification and keywords extraction to their tweets. To evaluate the results, we suggested a method for sampling the general population of Twitter users and forming a control group. We found statistically significant differences between the thematic segmentations of the patients and those of random Twitter users. We identified keywords that patients frequently use in the contexts of health, fitness, or nutrition, and obtained their sentiment. The results of the research suggest that the personal information shared by IBD patients on Twitter can be used to understand better the disease and how it affects patients’ lives. By leveraging posts describing patients’ daily activities and how they influence their wellbeing, we can derive complementary knowledge about the disease that is based on the wisdom of the crowd.

Citations (3)


... Furthermore, the stigmatization associated with IBD, at par or even higher than that with HIV/AIDS or diabetes, and the sense of embarrassment due to the disruption of normal activities drive most patients with IBD toward social media to obtain peer support and disease-related information and to communicate with their physicians or IBD support-related organizations [13,14]. Despite the presence of several online platforms, the easy accessibility and succinct information provided by X make it a noteworthy medium for providing a symbiotic, direct, and bidirectional flow of health-promoting information that transcends the walls of the clinic [15,16]. Furthermore, this interdependent procurement of information enables physicians to be up-to-date about trends in contemporary medicine. ...

Reference:

Inflammatory Bowel Disease and X (Formerly Twitter) Influencers: Who Are They and What Do They Say?
Identifying Patients on Twitter and Learning from Their Personal Experience: The Case of IBD (Preprint)

Journal of Medical Internet Research

... Enriching the dataset by identifying more patients on Twitter or expanding the search to other social media could improve the classifier's performance and precise classification. In a previous study, we identified 337 IBD patients who actively discussed their disease on Twitter (Stemmer, Parmet, & Ravid, 2021). In future research, we intend to add these patients to our model after determining the values of the classification features by mining the patients' Twitter timelines. ...

What Are IBD Patients Talking About on Twitter?
  • Citing Chapter
  • January 2021

Communications in Computer and Information Science

... The studies selected for this review underscored significant variations in protein expression linked to health conditions, predominantly related to carbohydrate metabolism and inflammation response, indicating the profound impact of host-microbiota interplay in pathological states. This interplay has implications for the activation of peripheral blood mononuclear cells, cytokine release, and the production of inflammation-related serum peptides and metabolites (10)(11)(12)(13)(14). ...

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