Monica Agrawal

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 34

Top Topics

Clinical Information

Entity Extraction

Video Endoscopy

Top Venues

Publications

Niklas Mannhardt, Elizabeth Bondi-Kelly, Barbara Lam, Chloe O'Connell, Mercy Asiedu, Hussein Mozannar, Monica Agrawal, Alejandro Buendia, Tatiana Urman, Irbaz B. Riaz, Catherine E. Ricciardi, Marzyeh Ghassemi, David A. Sontag
Impact of Large Language Model Assistance on Patients Reading Clinical Notes: A Mixed-Methods Study. CoRR (2024)
Sharon Jiang, Barbara D. Lam, Monica Agrawal, Shannon Shen, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing. J. Am. Medical Informatics Assoc. 31 (7) (2024)
Stefan Hegselmann, Shannon Zejiang Shen, Florian Gierse, Monica Agrawal, David A. Sontag, Xiaoyi Jiang
A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models. CoRR (2024)
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai "Orson" Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. CoRR (2024)
Emma Pierson, Divya Shanmugam, Rajiv Movva, Jon M. Kleinberg, Monica Agrawal, Mark Dredze, Kadija Ferryman, Judy Wawira Gichoya, Dan Jurafsky, Pang Wei Koh, Karen Levy, Sendhil Mullainathan, Ziad Obermeyer, Harini Suresh, Keyon Vafa
Use large language models to promote equity. CoRR (2023)
Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara Lam, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. MLHC (2023)
Sharon Jiang, Shannon Shen, Monica Agrawal, Barbara Lam, Nicholas Kurtzman, Steven Horng, David R. Karger, David A. Sontag
Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes. CoRR (2023)
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag
TabLLM: Few-shot Classification of Tabular Data with Large Language Models. AISTATS (2023)
Antonio Parziale, Monica Agrawal, Shengpu Tang, Kristen Severson, Luis Oala, Adarsh Subbaswamy, Sayantan Kumar, Elora D. M. Schörverth, Stefan Hegselmann, Helen Zhou, Ghada Zamzmi, Purity Mugambi, Elena Sizikova, Girmaw Abebe Tadesse, Yuyin Zhou, Taylor W. Killian, Haoran Zhang, Fahad Kamran, Andrea Hobby, Mars Huang, Ahmed Alaa, Harvineet Singh, Irene Y. Chen, Shalmali Joshi
Machine Learning for Health (ML4H) 2022. ML4H@NeurIPS (2022)
Stefan Hegselmann, Alejandro Buendia, Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David A. Sontag
TabLLM: Few-shot Classification of Tabular Data with Large Language Models. CoRR (2022)
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag
Large Language Models are Zero-Shot Clinical Information Extractors. CoRR (2022)
Hunter Lang, Monica Agrawal, Yoon Kim, David A. Sontag
Co-training Improves Prompt-based Learning for Large Language Models. CoRR (2022)
Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy
Machine Learning for Health symposium 2022 - Extended Abstract track. CoRR (2022)
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, David A. Sontag
Large language models are few-shot clinical information extractors. EMNLP (2022)
Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David A. Sontag
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. CoRR (2021)
Luke S. Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David A. Sontag, David R. Karger
MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. UIST (2021)
Monica Agrawal, Hunter Lang, Michael Offin, Lior Gazit, David A. Sontag
Leveraging Time Irreversibility with Order-Contrastive Pre-training. CoRR (2021)
Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. AISTATS (2021)
Ariel Levy, Monica Agrawal, Arvind Satyanarayan, David A. Sontag
Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative. CHI (2021)
Jason Zhao, Monica Agrawal, Pedram Razavi, David A. Sontag
Directing Human Attention in Event Localization for Clinical Timeline Creation. MLHC (2021)
Luke S. Murray, Divya Gopinath, Monica Agrawal, Steven Horng, David A. Sontag, David R. Karger
MedKnowts: Unified Documentation and Information Retrieval for Electronic Health Records. CoRR (2021)
Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag
Fast, Structured Clinical Documentation via Contextual Autocomplete. CoRR (2020)
Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag
Robust Benchmarking for Machine Learning of Clinical Entity Extraction. CoRR (2020)
Irene Y. Chen, Monica Agrawal, Steven Horng, David A. Sontag
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health KnowledgeGraph. PSB (2020)
Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash K. Mansinghka
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. CoRR (2020)
Benjamin Birnbaum, Nathan Nussbaum, Katharina Seidl-Rathkopf, Monica Agrawal, Melissa Estevez, Evan Estola, Joshua Haimson, Lucy He, Peter Larson, Paul Richardson
Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research. CoRR (2020)
Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag
Robust Benchmarking for Machine Learning of Clinical Entity Extraction. MLHC (2020)
Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag
Fast, Structured Clinical Documentation via Contextual Autocomplete. MLHC (2020)
Irene Y. Chen, Monica Agrawal, Steven Horng, David A. Sontag
Robustly Extracting Medical Knowledge from EHRs: A Case Study of Learning a Health Knowledge Graph. CoRR (2019)
Marinka Zitnik, Monica Agrawal, Jure Leskovec
Modeling polypharmacy side effects with graph convolutional networks. CoRR (2018)
Monica Agrawal, Griffin Adams, Nathan Nussbaum, Benjamin Birnbaum
TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes. CoRR (2018)
Marinka Zitnik, Monica Agrawal, Jure Leskovec
Modeling polypharmacy side effects with graph convolutional networks. Bioinform. 34 (13) (2018)
Monica Agrawal, Marinka Zitnik, Jure Leskovec
Large-scale analysis of disease pathways in the human interactome. PSB (2018)
Monica Agrawal, Marinka Zitnik, Jure Leskovec
Large-scale analysis of disease pathways in the human interactome. CoRR (2017)