We are seeking funds for supporting diversity students for a 2-year Research Experience at the Health Language Processing (HLP) Center at the University of Pennsylvania. Given the current pandemic, we will be running the experience fully remotely the first year, with flexible scheduling as outlined in the sections ahead, and with a mix of in-person and remote components for the second year. Targeted participants are talented diversity high school, undergraduate and PhD students from institutions across the country. In general, students participate in vertical research teams led by a post-doctoral research associate already involved in the parent grant (with no additional funding from the supplement). Teams include 1 PhD student, 1 undergraduate student, and 1-2 high school students. We have budgeted for 3 teams in this supplement. The diversity supplement participants will work alongside other students funded internally by the University of Pennsylvania or other sources whenever possible to enhance the experience. Each team is assigned a project or task relevant to the aims of the parent grant. High School students and Undergraduates contribute primarily as annotators, unless their skillset is such that they can contribute to code development or data analysis. We include a task that is more oriented to data analysis to accommodate those without a programming background. All students participate in enrichment activities (lectures, seminars, discussion groups, and scientific writing session) and present their work to peers, mentors, and the PI.

Public Health Relevance

This project seeks to support diversity high school, undergraduate and PhD students from across the country for a 2-year Research Experience at the Health Language Processing (HLP) Center at the University of Pennsylvania. The experience runs remotely the first year, and as a mix of in-person and remote components for the second year, with students organized in research teams (1 PhD student, 1 undergraduate student, and 1-2 high school students) led by a post-doctoral research associate already involved in the parent grant (with no additional funding from the supplement). Each team is assigned a project or task relevant to the aims of the parent grant, completing a manuscripts on the task at the end of each year while developing their research and leadership skills, as well as mentoring through enrichment activities (lectures, seminars, discussion groups, and scientific writing session), culminating with a presentation of their work in front of their peers, mentors, the PI and other members of the scientific community.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
3R01LM011176-08S1
Application #
10195888
Study Section
Program Officer
Vanbiervliet, Alan
Project Start
2012-09-10
Project End
2022-05-31
Budget Start
2020-08-01
Budget End
2021-05-31
Support Year
8
Fiscal Year
2020
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Smith, Karen; Golder, Su; Sarker, Abeed et al. (2018) Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab. Drug Saf 41:1397-1410
Sarker, Abeed; Gonzalez-Hernandez, Graciela (2018) An unsupervised and customizable misspelling generator for mining noisy health-related text sources. J Biomed Inform 88:98-107
Klein, Ari Z; Sarker, Abeed; Cai, Haitao et al. (2018) Social media mining for birth defects research: A rule-based, bootstrapping approach to collecting data for rare health-related events on Twitter. J Biomed Inform 87:68-78
Sarker, Abeed; Nikfarjam, Azadeh; Gonzalez, Graciela (2016) SOCIAL MEDIA MINING SHARED TASK WORKSHOP. Pac Symp Biocomput 21:581-92
Sarker, Abeed; O'Connor, Karen; Ginn, Rachel et al. (2016) Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter. Drug Saf 39:231-40
Sullivan, Ryan; Sarker, Abeed; O'Connor, Karen et al. (2016) FINDING POTENTIALLY UNSAFE NUTRITIONAL SUPPLEMENTS FROM USER REVIEWS WITH TOPIC MODELING. Pac Symp Biocomput 21:528-39
Korkontzelos, Ioannis; Nikfarjam, Azadeh; Shardlow, Matthew et al. (2016) Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts. J Biomed Inform 62:148-58
Gonzalez, Graciela H; Tahsin, Tasnia; Goodale, Britton C et al. (2016) Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery. Brief Bioinform 17:33-42
Paul, Michael J; Sarker, Abeed; Brownstein, John S et al. (2016) SOCIAL MEDIA MINING FOR PUBLIC HEALTH MONITORING AND SURVEILLANCE. Pac Symp Biocomput 21:468-79
Sarker, Abeed; Ginn, Rachel; Nikfarjam, Azadeh et al. (2015) Utilizing social media data for pharmacovigilance: A review. J Biomed Inform 54:202-12

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