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Caitlin N. Dreisbach
2020 – today
- 2024
- [j6]Theresa A. Koleck
, Caitlin N. Dreisbach
, Chen Zhang, Susan Grayson, Maichou Lor
, Zhirui Deng, Alex Conway, Peter D. R. Higgins, Suzanne Bakken
:
User guide for Social Determinants of Health Survey data in the All of Us Research Program. J. Am. Medical Informatics Assoc. 31(12): 3032-3041 (2024) - [c6]Suzanne Bakken
, Caitlin N. Dreisbach
, Meghan Reading Turchioe
:
Advancing Data Science Competencies for Nursing PhD Students. Nursing Informatics 2024: 515-519 - 2023
- [j5]Betina Ross S. Idnay
, Yilu Fang
, Caitlin N. Dreisbach
, Karen Marder, Chunhua Weng, Rebecca Schnall
:
Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment. Int. J. Medical Informatics 171: 104985 (2023) - 2022
- [c5]Betina Ross S. Idnay, Yilu Fang, Caitlin N. Dreisbach, Karen Marder, Chunhua Weng, Rebecca Schnall:
Optimizing Clinical Research Eligibility Prescreening: An Iterative Usability Evaluation of an NLP-driven Cohort Identification Tool. AMIA 2022 - [c4]Theresa A. Koleck, Susan Grayson, Katelyn Leggio, Alex Conway, Caitlin N. Dreisbach:
Symptom Phenotypes and Predictors of Unrelieved Symptoms in Individuals with Chronic Conditions: A Cross-sectional Analysis of the All of Us Research Program Survey Data. AMIA 2022 - [i2]Daphna Spira, Noreen Mayat, Caitlin N. Dreisbach, Adam Poliak:
Discovering changes in birthing narratives during COVID-19. CoRR abs/2204.11742 (2022) - 2021
- [j4]Betina Ross S. Idnay
, Caitlin N. Dreisbach
, Chunhua Weng, Rebecca Schnall
:
A systematic review on natural language processing systems for eligibility prescreening in clinical research. J. Am. Medical Informatics Assoc. 29(1): 197-206 (2021) - [c3]Caitlin N. Dreisbach, Katherine South, Theresa A. Koleck, Veronica Barcelona, Lena Mamykina, Noémie Elhadad, Suzanne Bakken:
Informing Symptom Science Using a Citizen Science Application in the COVID-19 Pandemic. AMIA 2021 - [c2]Max Fleming, Priyanka Dondeti, Caitlin N. Dreisbach, Adam Poliak:
Fine-tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets. SMM4H@NAACL-HLT 2021: 131-134 - [i1]Max Fleming, Priyanka Dondeti, Caitlin N. Dreisbach, Adam Poliak:
Fine-Tuning Transformers for Identifying Self-Reporting Potential Cases and Symptoms of COVID-19 in Tweets. CoRR abs/2104.05501 (2021)
2010 – 2019
- 2019
- [j3]Caitlin N. Dreisbach
, Theresa A. Koleck, Philip E. Bourne
, Suzanne Bakken:
A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. Int. J. Medical Informatics 125: 37-46 (2019) - [j2]Theresa A. Koleck, Caitlin N. Dreisbach
, Philip E. Bourne, Suzanne Bakken
:
Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review. J. Am. Medical Informatics Assoc. 26(4): 364-379 (2019) - 2018
- [j1]Elizabeth Harrison
, Caitlin N. Dreisbach
, Nada Basit, Jessica Keim-Malpass:
An Application of Data Mining Techniques to Explore Congressional Lobbying Records for Patterns in Pediatric Special Interest Expenditures Prior to the Affordable Care Act. Frontiers Big Data 1: 3 (2018) - [c1]Alicia L. Nobles, Caitlin N. Dreisbach, Jessica Keim-Malpass, Laura E. Barnes:
"Is This an STD? Please Help!": Online Information Seeking for Sexually Transmitted Diseases on Reddit. ICWSM 2018: 660-663
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last updated on 2025-01-09 19:35 CET by the dblp team
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