Banda, J. M. | bioRxiv Science

bioRxiv2019

Mining Archive.org's Twitter Stream Grab for Pharmacovigilance Research Gold

In the last few years Twitter has become an important resource for the identification of Adverse Drug Reactions (ADRs), monitoring flu trends, and other pharmacovigilance and general research applications. Most researchers spend their time crawling Twitter, buying expensive pre-mined datasets, or tediously and slowly building datasets using the limited Twitter API. However, there are a large number of datasets that are publicly available to researchers which are underutilized or unused. In this work, we demonstrate how we mined over 9.4 billion Tweets from archive.orgs Twitter stream grab using a drug-term dictionary and plenty of computing power. Knowing that not everything that shines is gold, we used pre-existing drug-related datasets to build machine learning models to filter our findings for relevance. In this work we present our methodology and the 3,346,758 identified tweets for public use in future research.

bioinformatics↗

bioRxiv2019

Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) Network

ObjectiveAccurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed ten phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Sciences and Informatics (OHDSI) network.\n\nMaterials and MethodsWe constructed classifiers using the Automated PHenotype Routine for Observational Definition, Identification, Training and Evaluation (APHRODITE) R-package, an open-source framework for learning phenotype classifiers using datasets in the OMOP CDM. We labeled training data based on the presence of multiple mentions of disease-specific codes. Performance was evaluated on cohorts derived using rule-based definitions and real-world disease prevalence. Classifiers were developed and evaluated across three medical centers, including one international site.\n\nResultsCompared to the multiple mentions labeling heuristic, classifiers showed a mean recall boost of 0.43 with a mean precision loss of 0.17. Performance decreased slightly when classifiers were shared across medical centers, with mean recall and precision decreasing by 0.08 and 0.01, respectively, at a site within the USA, and by 0.18 and 0.10, respectively, at an international site.\n\nDiscussion and ConclusionWe demonstrate a high-throughput pipeline for constructing and sharing phenotype classifiers across multiple sites within the OHDSI network using APHRODITE. Classifiers exhibit good portability between sites within the USA, however limited portability internationally, indicating that classifier generalizability may have geographic limitations, and consequently, sharing the classifier-building recipe, rather than the pre-trained classifiers, may be more useful for facilitating collaborative observational research.

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