🧑‍🔬 Monica Agrawal

🏛️ Affiliations

🎓Duke UniversityDurham · United States2024–2026 · 24 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2018–2025 · 16 papers🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2021–2024 · 6 papers🎓Stanford UniversityStanford · United States2014–2023 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Large language models are few-shot clinical information extractors (2022 · 454 citations)
   Extracting structured variables from clinical notes is difficult. The text is full of jargon and irregular phrasing, annotation requires medical experts, and most annotated corpora have data use agreements that forbid sending them to external LLM APIs. The authors ask whether a general-domain model, InstructGPT…
2. Large Language Models are Few-Shot Clinical Information Extractors (2022 · 384 citations)
   Clinical notes contain many variables that never reach the structured fields of electronic health records.
3. MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health Communication (2026)
   Patients often build their health questions on mistaken beliefs, such as trusting an implausible wearable reading or assuming a rare diagnosis.

All works

7 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Modeling polypharmacy side effects with graph convolutional networks ↗Bioinformatics · no summary yetMarinka Žitnik, Monica Agrawal, Jure Leskovec2018 · 1,517 citations
2. Large language models are few-shot clinical information extractorsConference on Empirical Methods in Natural Language Processing (EMNLP)Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.2022 · 454 citations
3. Large Language Models are Few-Shot Clinical Information ExtractorsConference on Empirical Methods in Natural Language Processing (EMNLP)Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.2022 · 384 citations
4. Large-scale analysis of disease pathways in the human interactome ↗Pacific Symposium on Biocomputing (PSB) · no summary yetMonica Agrawal, Marinka Žitnik, Jure Leskovec2017 · 106 citations
5. Co-training Improves Prompt-based Learning for Large Language Models ↗arXiv (Cornell University) · no summary yetHunter Lang et al.2022 · 41 citations
6. Using Large Language Models to Promote Health Equity ↗NEJM AI · no summary yetEmma Pierson et al.2023 · 18 citations
7. MedRedFlag: Investigating how LLMs Redirect Misconceptions in Real-World Health CommunicationFindings of the Association for Computational LinguisticsSraavya Sambara, Yuan Pu, Ayman Ali, et al.2026

🤝 Co-authors here

5 co-authors with a page, the most papers together first.

• David Sontag ×2
• Hunter Lang ×2
• Stefan Hegselmann ×2
• Yoon Kim ×2
• Lionel Wong

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2022 ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Bioinformatics
• NEJM AI
• Pacific Symposium on Biocomputing (PSB)

🔗 Cited by

Papers of our library cite their works 205 times (of the 149,439 papers whose bibliographies we read); 34 citing papers with a page here →.

Updated 1 November 2026

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