Performing research in pregnancy: Challenges and perspectives

Hartman, Rebecca I.; Kimball, Alexa B.

doi:10.1016/j.clindermatol.2016.02.014

Cited by 5 publications

(3 citation statements)

References 19 publications

(33 reference statements)

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“…According to the United States Centers for Disease Control and Prevention (CDC), birth defects are the leading cause of infant mortality in the United States [1], likely because the etiology of the majority of birth defects remains unknown [2]. Closing this knowledge gap has been challenging because methods for studying birth defects are limited; for example, pregnant women are largely excluded from clinical trials [3,4], animal reproductive studies may not translate to human risk factors [5,6], and pregnancy exposure registries [7] have suffered from selection bias (e.g., enrolling women who have had prenatal testing with normal results) [8], lack of internal comparator groups [8], and short follow-up periods, which can lead to an under-assessment of birth defects because not all are recognized at birth [9]. Given these methodological limitations, additional methods for observing pregnancies with birth defect outcomes should be explored to complement existing methods for studying birth defects.…”

Section: Introductionmentioning

confidence: 99%

Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter

Klein

Sarker

Cai

et al. 2018

Journal of Biomedical Informatics

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Background: Although birth defects are the leading cause of infant mortality in the United States, methods for observing human pregnancies with birth defect outcomes are limited. Objective: The primary objectives of this study were (i) to assess whether rare health-related events—in this case, birth defects—are reported on social media, (ii) to design and deploy a natural language processing (NLP) approach for collecting such sparse data from social media, and (iii) to utilize the collected data to discover a cohort of women whose pregnancies with birth defect outcomes could be observed on social media for epidemiological analysis. Methods: To assess whether birth defects are mentioned on social media, we mined 432 million tweets posted by 112,647 users who were automatically detected via their public announcements of pregnancies on Twitter. To retrieve tweets that mention birth defects, we developed a rule-based, bootstrapping approach, which relies on a lexicon, lexical variants generated from the lexicon entries, regular expressions, post-processing, and manual analysis guided by distributional properties. To identify users whose pregnancies with birth defect outcomes could be observed for epidemiological analysis, inclusion criteria were (i) tweets indicating that the user’s child has a birth defect, and (ii) accessibility to the user’s tweets during pregnancy. We conducted a semi-automatic evaluation to estimate the recall of the tweet-collection approach, and performed a preliminary assessment of the prevalence of selected birth defects among the pregnancy cohort derived from Twitter. Results: We manually annotated 16,822 retrieved tweets, distinguishing tweets indicating that the user’s child has a birth defect (true positives) from tweets that merely mention birth defects (false positives). Inter-annotator agreement was substantial: κ = 0.79 (Cohen’s kappa). Analyzing the timelines of the 646 users whose tweets were true positives resulted in the discovery of 195 users that met the inclusion criteria. Congenital heart defects are the most common type of birth defect reported on Twitter, consistent with findings in the general population. Based on an evaluation of 4,169 tweets retrieved using alternative text mining methods, the recall of the tweet-collection approach was 0.95. Conclusions: Our contributions include (i) evidence that rare health-related events are indeed reported on Twitter, (ii) a generalizable, systematic NLP approach for collecting sparse tweets, (iii) a semi-automatic method to identify undetected tweets (false negatives), and (iv) a collection of publicly available tweets by pregnant users with birth defect outcomes, which could be used for future epidemiological analysis. In future work, the annotated tweets could be used to train machine learning algorithms to automatically identify users reporting birth defect outcomes, enabling the large-scale use of social media mining as a complementary method for such epidemiological research.

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Section: Introductionmentioning

confidence: 99%

Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter

Klein

Sarker

Cai

et al. 2018

Journal of Biomedical Informatics

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“…Despite the fact that birth defects are the leading cause of infant mortality in the United States, 1 methods for observing pregnancies with birth defect outcomes remain limited (e.g., clinical trials, 2,3 animal studies, 4 pregnancy exposure registries 5–8 ). Considering that 40% of US adults between ages 18 and 29 use Twitter, 9 in recent work, 10 we sought to identify a cohort of women whose pregnancies with birth defect outcomes could be observed via their publicly available tweets.…”

Section: Introductionmentioning

confidence: 99%

Towards scaling Twitter for digital epidemiology of birth defects

et al. 2019

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Social media has recently been used to identify and study a small cohort of Twitter users whose pregnancies with birth defect outcomes—the leading cause of infant mortality—could be observed via their publicly available tweets. In this study, we exploit social media on a larger scale by developing natural language processing (NLP) methods to automatically detect, among thousands of users, a cohort of mothers reporting that their child has a birth defect. We used 22,999 annotated tweets to train and evaluate supervised machine learning algorithms—feature-engineered and deep learning-based classifiers—that automatically distinguish tweets referring to the user’s pregnancy outcome from tweets that merely mention birth defects. Because 90% of the tweets merely mention birth defects, we experimented with under-sampling and over-sampling approaches to address this class imbalance. An SVM classifier achieved the best performance for the two positive classes: an F1-score of 0.65 for the “defect” class and 0.51 for the “possible defect” class. We deployed the classifier on 20,457 unlabeled tweets that mention birth defects, which helped identify 542 additional users for potential inclusion in our cohort. Contributions of this study include (1) NLP methods for automatically detecting tweets by users reporting their birth defect outcomes, (2) findings that an SVM classifier can outperform a deep neural network-based classifier for highly imbalanced social media data, (3) evidence that automatic classification can be used to identify additional users for potential inclusion in our cohort, and (4) a publicly available corpus for training and evaluating supervised machine learning algorithms.

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“…This design of pregnancy exposure registries enables researchers to conduct prospective observational studies, which are superior to retrospective studies because of the biases associated with the latter (eg, the outcome, such as birth defect, is already known in retrospective studies) [ 2 ]. Thus, the model followed by pregnancy exposure registries has distinct advantages over other study designs, because these registries can produce human data regarding medication safety in pregnancy while avoiding the ethical and logistical pitfalls of randomized controlled trials [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis

Sarker¹,

Chandrashekar²,

Magge³

et al. 2017

J Med Internet Res

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BackgroundPregnancy exposure registries are the primary sources of information about the safety of maternal usage of medications during pregnancy. Such registries enroll pregnant women in a voluntary fashion early on in pregnancy and follow them until the end of pregnancy or longer to systematically collect information regarding specific pregnancy outcomes. Although the model of pregnancy registries has distinct advantages over other study designs, they are faced with numerous challenges and limitations such as low enrollment rate, high cost, and selection bias.ObjectiveThe primary objectives of this study were to systematically assess whether social media (Twitter) can be used to discover cohorts of pregnant women and to develop and deploy a natural language processing and machine learning pipeline for the automatic collection of cohort information. In addition, we also attempted to ascertain, in a preliminary fashion, what types of longitudinal information may potentially be mined from the collected cohort information.MethodsOur discovery of pregnant women relies on detecting pregnancy-indicating tweets (PITs), which are statements posted by pregnant women regarding their pregnancies. We used a set of 14 patterns to first detect potential PITs. We manually annotated a sample of 14,156 of the retrieved user posts to distinguish real PITs from false positives and trained a supervised classification system to detect real PITs. We optimized the classification system via cross validation, with features and settings targeted toward optimizing precision for the positive class. For users identified to be posting real PITs via automatic classification, our pipeline collected all their available past and future posts from which other information (eg, medication usage and fetal outcomes) may be mined.ResultsOur rule-based PIT detection approach retrieved over 200,000 posts over a period of 18 months. Manual annotation agreement for three annotators was very high at kappa (κ)=.79. On a blind test set, the implemented classifier obtained an overall F1 score of 0.84 (0.88 for the pregnancy class and 0.68 for the nonpregnancy class). Precision for the pregnancy class was 0.93, and recall was 0.84. Feature analysis showed that the combination of dense and sparse vectors for classification achieved optimal performance. Employing the trained classifier resulted in the identification of 71,954 users from the collected posts. Over 250 million posts were retrieved for these users, which provided a multitude of longitudinal information about them.ConclusionsSocial media sources such as Twitter can be used to identify large cohorts of pregnant women and to gather longitudinal information via automated processing of their postings. Considering the many drawbacks and limitations of pregnancy registries, social media mining may provide beneficial complementary information. Although the cohort sizes identified over social media are large, future research will have to assess the completeness of the information available through them.

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Performing research in pregnancy: Challenges and perspectives

Cited by 5 publications

References 19 publications

Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter

Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter

Towards scaling Twitter for digital epidemiology of birth defects

Discovering Cohorts of Pregnant Women From Social Media for Safety Surveillance and Analysis

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