---
title: "Large language models are few-shot clinical — Agrawal 2022"
description: "Extracting structured variables from clinical notes is difficult."
authors: ["Monica Agrawal", "Stefan Hegselmann", "Hunter Lang", "Yoon Kim", "David Sontag"]
year: 2022
venue: "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2022.emnlp-main.130/"
doi: "10.18653/v1/2022.emnlp-main.130"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/) / Large language models are few-shot clinical information extractors

# Large language models are few-shot clinical information extractors

**Authors:** Monica Agrawal, Stefan Hegselmann, Hunter Lang, [Yoon Kim](https://md.research.elamur.ai/free/en/authors/yoon-kim/), [David Sontag](https://md.research.elamur.ai/free/en/authors/david-sontag/) · **Year:** 2022 · **Venue:** Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2022.emnlp-main.130/) · [PDF](https://aclanthology.org/2022.emnlp-main.130.pdf) · [DOI 10.18653/v1/2022.emnlp-main.130](https://doi.org/10.18653/v1/2022.emnlp-main.130) · [OpenAlex](https://openalex.org/W4385573087)

**Citations:** 348 citations (openalex-snapshot · 2026-10-14) · [100](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
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 (GPT-3), can handle clinical information extraction with no examples or only one. They study five tasks: working out which sense an acronym carries, identifying evidence in biomedical abstracts, linking pronouns to their antecedents (coreference), determining the status of each medication, and pulling out the attributes of medications. Several of these need structured outputs such as spans, token labels or relations, not just a class label. To get such outputs, they propose guided prompt design, which uses a one-shot example in the desired format plus an instruction to follow that format. Lightweight "resolvers" then map the generated text onto the task's label space. They also release new benchmarks, built by re-annotating the public CASI corpus. Across the studied tasks, the resolved GPT-3 systems clearly beat existing zero- and few-shot baselines. This suggests that LLMs can replace much of the hand-built domain knowledge in clinical extraction pipelines.

## Key points
- **New datasets (on Hugging Face as `mitclinicalml/clinical-ie`), all derived from CASI (Moon et al., 2014):**
  - coreference: 105 examples, each with one pronoun–antecedent pair;
  - medication status: 105 examples with 340 medication–status pairs;
  - medication attributes: 105 examples with 313 medications and 533 attributes.
- **Other data used:**
  - acronym disambiguation: 41 acronyms drawn from 18,164 CASI notes and 8,912 MIMIC notes;
  - evidence extraction: 187 EBM-NLP abstracts, plus 20 newly annotated abstracts for arm identification.
- **Setup:** the model is used with query access only (no gradients or log probabilities). Prompt templates were handcrafted using just 5 validation examples per task. Resolver complexity is reported in lines of code.
- **Limitations:**
  - The model struggles to match exact annotation schemas, e.g., outputting "X weeks" instead of "for X weeks".
  - It tends to produce a non-empty answer even when no correct answer exists.
  - Evaluation was English-only and limited to CASI-derived text.
- **Deployment:** the authors suggest using GPT-3 pseudolabels as weak supervision to distill smaller, cheaper, more deployable models.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

100 papers of our library cite it (of the 140884 whose bibliographies we read); 7 of them have a page here, 93 not yet.

- [Are Large Language Models Robust Coreference Resolvers?](https://md.research.elamur.ai/free/en/papers/le-2023-large-language-models-robust-coreference/)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/)
- [Major Entity Identification: A Generalizable Alternative to Coreference Resolution](https://md.research.elamur.ai/free/en/papers/manikantan-2024-major-entity-identification-generalizable-alternative/)
- [Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://md.research.elamur.ai/free/en/papers/pal-2023-med-halt-medical-domain-hallucination/)
- [Faithful Medical Dialogue Generation Using Homo-Heterogeneous Exemplar-based In-Context Knowledge Grounding](https://md.research.elamur.ai/free/en/papers/priya-2026-faithful-medical-dialogue-generation-homo/)
- [Multilingual coreference resolution as text generation](https://md.research.elamur.ai/free/en/papers/skachkova-2024-multilingual-coreference-resolution-as-text/)
- [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://md.research.elamur.ai/free/en/papers/zaratiana-2024-gliner-generalist-model-named-entity/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{agrawal2022large,
  title = {{Large language models are few-shot clinical information extractors}},
  author = {Agrawal, Monica and Hegselmann, Stefan and Lang, Hunter and Kim, Yoon and Sontag, David},
  booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
  year = {2022},
  doi = {10.18653/v1/2022.emnlp-main.130},
  url = {https://doi.org/10.18653/v1/2022.emnlp-main.130}
}
```

- [← previous Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
- [next → Large Language Models Are Human-Level Prompt Engineers](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/)
- [↑ back to papers of 2022](https://md.research.elamur.ai/free/en/papers/2022/page/3/)

## References

80 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Griffin Adams, Mert Ketenci, Shreyas Bhave, Adler J. Perotte, and Noémie Elhadad. 2020. Zero-shot clinical acronym expansion via latent meaning cells. In Machine Learning for Health Workshop, ML4H@NeurIPS 2020, Virtual Event, 11 December 2020, volume 136 of Proceedings of Machine Learning Research,… [find](https://scholar.google.com/scholar?q=Griffin+Adams%2C+Mert+Ketenci%2C+Shreyas+Bhave%2C+Adler+J%2E+Perotte%2C+and+No%C3%A9mie+Elhadad%2E+2020%2E+Zero%2Dshot+clinical+acronym+expansion+via+latent+meaning+cells%2E+In+Machine+Learning+for+Health+Workshop%2C+ML4H%40Neu)
2. Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical bert embeddings. arXiv preprint arXiv:1904.03323. [find](https://scholar.google.com/scholar?q=Emily+Alsentzer%2C+John+R+Murphy%2C+Willie+Boag%2C+Wei%2DHung+Weng%2C+Di+Jin%2C+Tristan+Naumann%2C+and+Matthew+McDermott%2E+2019%2E+Publicly+available+clinical+bert+embeddings%2E+arXiv+preprint+arXiv%3A1904%2E03323%2E) [our card](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/)
3. Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, et al. 2018. Construction of the literature graph in semantic scholar. arXiv preprint arXiv:1805.02262. [find](https://scholar.google.com/scholar?q=Waleed+Ammar%2C+Dirk+Groeneveld%2C+Chandra+Bhagavatula%2C+Iz+Beltagy%2C+Miles+Crawford%2C+Doug+Downey%2C+Jason+Dunkelberger%2C+Ahmed+Elgohary%2C+Sergey+Feldman%2C+Vu+Ha%2C+et+al%2E+2018%2E+Construction+of+the+literature+grap) [our card](https://md.research.elamur.ai/free/en/papers/ammar-2018-construction-literature-graph-semantic-scholar/)
4. Hilda Bastian, Paul Glasziou, and Iain Chalmers. 2010. Seventy-five trials and eleven systematic reviews a day: how will we ever keep up? PLoS medicine, 7(9):e1000326. [find](https://scholar.google.com/scholar?q=Hilda+Bastian%2C+Paul+Glasziou%2C+and+Iain+Chalmers%2E+2010%2E+Seventy%2Dfive+trials+and+eleven+systematic+reviews+a+day%3A+how+will+we+ever+keep+up%3F+PLoS+medicine%2C+7%289%29%3Ae1000326%2E)
5. Olivier Bodenreider. 2004. The unified medical language system (umls): integrating biomedical terminology. Nucleic acids research, 32(suppl_1):D267– D270. [find](https://scholar.google.com/scholar?q=Olivier+Bodenreider%2E+2004%2E+The+unified+medical+language+system+%28umls%29%3A+integrating+biomedical+terminology%2E+Nucleic+acids+research%2C+32%28suppl%5F1%29%3AD267%E2%80%93+D270%2E)
6. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. [find](https://scholar.google.com/scholar?q=Tom+Brown%2C+Benjamin+Mann%2C+Nick+Ryder%2C+Melanie+Subbiah%2C+Jared+D+Kaplan%2C+Prafulla+Dhariwal%2C+Arvind+Neelakantan%2C+Pranav+Shyam%2C+Girish+Sastry%2C+Amanda+Askell%2C+et+al%2E+2020%2E+Language+models+are+few%2Dshot+lear)
7. Wendy W Chapman, Will Bridewell, Paul Hanbury, Gregory F Cooper, and Bruce G Buchanan. 2001. A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of biomedical informatics, 34(5):301–310. [find](https://scholar.google.com/scholar?q=Wendy+W+Chapman%2C+Will+Bridewell%2C+Paul+Hanbury%2C+Gregory+F+Cooper%2C+and+Bruce+G+Buchanan%2E+2001%2E+A+simple+algorithm+for+identifying+negated+findings+and+diseases+in+discharge+summaries%2E+Journal+of+biomedi)
8. Geeticka Chauhan, Ruizhi Liao, William Wells, Jacob Andreas, Xin Wang, Seth Berkowitz, Steven Horng, Peter Szolovits, and Polina Golland. 2020. Joint modeling of chest radiographs and radiology reports for pulmonary edema assessment. In International Conference on Medical Image Computing and Compute… [find](https://scholar.google.com/scholar?q=Geeticka+Chauhan%2C+Ruizhi+Liao%2C+William+Wells%2C+Jacob+Andreas%2C+Xin+Wang%2C+Seth+Berkowitz%2C+Steven+Horng%2C+Peter+Szolovits%2C+and+Polina+Golland%2E+2020%2E+Joint+modeling+of+chest+radiographs+and+radiology+report)
9. Irene Y Chen, Emily Alsentzer, Hyesun Park, Richard Thomas, Babina Gosangi, Rahul Gujrathi, and Bharti Khurana. 2020. Intimate partner violence and injury prediction from radiology reports. In BIOCOMPUT-ING 2021: Proceedings of the Pacific Symposium, pages 55–66. World Scientific. [find](https://scholar.google.com/scholar?q=Irene+Y+Chen%2C+Emily+Alsentzer%2C+Hyesun+Park%2C+Richard+Thomas%2C+Babina+Gosangi%2C+Rahul+Gujrathi%2C+and+Bharti+Khurana%2E+2020%2E+Intimate+partner+violence+and+injury+prediction+from+radiology+reports%2E+In+BIOCOMP)
10. Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021. Template-based named entity recognition using bart. arXiv preprint arXiv:2106.01760. [find](https://scholar.google.com/scholar?q=Leyang+Cui%2C+Yu+Wu%2C+Jian+Liu%2C+Sen+Yang%2C+and+Yue+Zhang%2E+2021%2E+Template%2Dbased+named+entity+recognition+using+bart%2E+arXiv+preprint+arXiv%3A2106%2E01760%2E) [our card](https://md.research.elamur.ai/free/en/papers/cui-2021-template-based-named-entity-recognition/)
11. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. [find](https://scholar.google.com/scholar?q=Jacob+Devlin%2C+Ming%2DWei+Chang%2C+Kenton+Lee%2C+and+Kristina+Toutanova%2E+2018%2E+Bert%3A+Pre%2Dtraining+of+deep+bidirectional+transformers+for+language+understanding%2E+arXiv+preprint+arXiv%3A1810%2E04805%2E)
12. Elisa Ferracane, Iain Marshall, Byron C Wallace, and Katrin Erk. 2016. Leveraging coreference to identify arms in medical abstracts: An experimental study. In Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis, pages 86–95. [find](https://scholar.google.com/scholar?q=Elisa+Ferracane%2C+Iain+Marshall%2C+Byron+C+Wallace%2C+and+Katrin+Erk%2E+2016%2E+Leveraging+coreference+to+identify+arms+in+medical+abstracts%3A+An+experimental+study%2E+In+Proceedings+of+the+Seventh+International+)
13. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1–23. [find](https://scholar.google.com/scholar?q=Yu+Gu%2C+Robert+Tinn%2C+Hao+Cheng%2C+Michael+Lucas%2C+Naoto+Usuyama%2C+Xiaodong+Liu%2C+Tristan+Naumann%2C+Jianfeng+Gao%2C+and+Hoifung+Poon%2E+2021%2E+Domain%2Dspecific+language+model+pretraining+for+biomedical+natural+lang) [our card](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
14. Bernal Jiménez Gutiérrez, Nikolas McNeal, Clay Washington, You Chen, Lang Li, Huan Sun, and Yu Su. 2022. Thinking about gpt-3 in-context learning for biomedical ie? think again. arXiv preprint arXiv:2203.08410. [find](https://scholar.google.com/scholar?q=Bernal+Jim%C3%A9nez+Guti%C3%A9rrez%2C+Nikolas+McNeal%2C+Clay+Washington%2C+You+Chen%2C+Lang+Li%2C+Huan+Sun%2C+and+Yu+Su%2E+2022%2E+Thinking+about+gpt%2D3+in%2Dcontext+learning+for+biomedical+ie%3F+think+again%2E+arXiv+preprint+arXiv%3A2)
15. Sam Henry, Kevin Buchan, Michele Filannino, Amber Stubbs, and Ozlem Uzuner. 2020. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association, 27(1):3–12. [find](https://scholar.google.com/scholar?q=Sam+Henry%2C+Kevin+Buchan%2C+Michele+Filannino%2C+Amber+Stubbs%2C+and+Ozlem+Uzuner%2E+2020%2E+2018+n2c2+shared+task+on+adverse+drug+events+and+medication+extraction+in+electronic+health+records%2E+Journal+of+the+Am)
16. Or Honovich, Uri Shaham, Samuel R Bowman, and Omer Levy. 2022. Instruction induction: From few examples to natural language task descriptions. arXiv preprint arXiv:2205.10782. [find](https://scholar.google.com/scholar?q=Or+Honovich%2C+Uri+Shaham%2C+Samuel+R+Bowman%2C+and+Omer+Levy%2E+2022%2E+Instruction+induction%3A+From+few+examples+to+natural+language+task+descriptions%2E+arXiv+preprint+arXiv%3A2205%2E10782%2E)
17. Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. 2019. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI conference on art… [find](https://scholar.google.com/scholar?q=Jeremy+Irvin%2C+Pranav+Rajpurkar%2C+Michael+Ko%2C+Yifan+Yu%2C+Silviana+Ciurea%2DIlcus%2C+Chris+Chute%2C+Henrik+Marklund%2C+Behzad+Haghgoo%2C+Robyn+Ball%2C+Katie+Shpanskaya%2C+et+al%2E+2019%2E+Chexpert%3A+A+large+chest+radiograph)
18. Min Jiang, Yukun Chen, Mei Liu, S Trent Rosenbloom, Subramani Mani, Joshua C Denny, and Hua Xu. 2011. [find](https://scholar.google.com/scholar?q=Min+Jiang%2C+Yukun+Chen%2C+Mei+Liu%2C+S+Trent+Rosenbloom%2C+Subramani+Mani%2C+Joshua+C+Denny%2C+and+Hua+Xu%2E+2011%2E)
19. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal of the American Medical Informatics Association, 18(5):601–606. [find](https://scholar.google.com/scholar?q=A+study+of+machine%2Dlearning%2Dbased+approaches+to+extract+clinical+entities+and+their+assertions+from+discharge+summaries%2E+Journal+of+the+American+Medical+Informatics+Association%2C+18%285%29%3A601%E2%80%93606%2E)
20. Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng. 2019. Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data, 6(1):1–8. [find](https://scholar.google.com/scholar?q=Alistair+EW+Johnson%2C+Tom+J+Pollard%2C+Seth+J+Berkowitz%2C+Nathaniel+R+Greenbaum%2C+Matthew+P+Lungren%2C+Chih%2Dying+Deng%2C+Roger+G+Mark%2C+and+Steven+Horng%2E+2019%2E+Mimic%2Dcxr%2C+a+de%2Didentified+publicly+available+data)
21. Andy T Liu, Wei Xiao, Henghui Zhu, Dejiao Zhang, Shang-Wen Li, and Andrew Arnold. 2022a. Qaner: Prompting question answering models for few-shot named entity recognition. arXiv preprint arXiv:2203.01543. — [arXiv:2203.01543](https://arxiv.org/abs/2203.01543) [find](https://scholar.google.com/scholar?q=Andy+T+Liu%2C+Wei+Xiao%2C+Henghui+Zhu%2C+Dejiao+Zhang%2C+Shang%2DWen+Li%2C+and+Andrew+Arnold%2E+2022a%2E+Qaner%3A+Prompting+question+answering+models+for+few%2Dshot+named+entity+recognition%2E+arXiv+preprint+arXiv%3A2203%2E015)
22. Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin Raffel. 2022b. Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning. arXiv preprint arXiv:2205.05638. [find](https://scholar.google.com/scholar?q=Haokun+Liu%2C+Derek+Tam%2C+Mohammed+Muqeeth%2C+Jay+Mohta%2C+Tenghao+Huang%2C+Mohit+Bansal%2C+and+Colin+Raffel%2E+2022b%2E+Few%2Dshot+parameter%2Defficient+fine%2Dtuning+is+better+and+cheaper+than+in%2Dcontext+learning%2E+arXiv) [our card](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/)
23. Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016. Mimic-iii, a freely accessible critical care database. Scientific data, 3(1):1–9. [find](https://scholar.google.com/scholar?q=Alistair+EW+Johnson%2C+Tom+J+Pollard%2C+Lu+Shen%2C+Li%2Dwei+H+Lehman%2C+Mengling+Feng%2C+Mohammad+Ghassemi%2C+Benjamin+Moody%2C+Peter+Szolovits%2C+Leo+Anthony+Celi%2C+and+Roger+G+Mark%2E+2016%2E+Mimic%2Diii%2C+a+freely+accessibl) [our card](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/)
24. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586. [find](https://scholar.google.com/scholar?q=Pengfei+Liu%2C+Weizhe+Yuan%2C+Jinlan+Fu%2C+Zhengbao+Jiang%2C+Hiroaki+Hayashi%2C+and+Graham+Neubig%2E+2021%2E+Pre%2Dtrain%2C+prompt%2C+and+predict%3A+A+systematic+survey+of+prompting+methods+in+natural+language+processing%2E+) [our card](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
25. Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey P Bigham, and Zachary C Lipton. 2021. Extracting structured data from physician-patient conversations by predicting noteworthy utterances. In Explainable AI in Healthcare and Medicine, pages 155–169. Springer. [find](https://scholar.google.com/scholar?q=Kundan+Krishna%2C+Amy+Pavel%2C+Benjamin+Schloss%2C+Jeffrey+P+Bigham%2C+and+Zachary+C+Lipton%2E+2021%2E+Extracting+structured+data+from+physician%2Dpatient+conversations+by+predicting+noteworthy+utterances%2E+In+Expla)
26. Ilya Loshchilov and Frank Hutter. 2018. Decoupled weight decay regularization. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Ilya+Loshchilov+and+Frank+Hutter%2E+2018%2E+Decoupled+weight+decay+regularization%2E+In+International+Conference+on+Learning+Representations%2E)
27. Ivy Fenton Kuhn. 2007. Abbreviations and acronyms in healthcare: when shorter isn’t sweeter. Pediatric nursing, 33(5). [find](https://scholar.google.com/scholar?q=Ivy+Fenton+Kuhn%2E+2007%2E+Abbreviations+and+acronyms+in+healthcare%3A+when+shorter+isn%E2%80%99t+sweeter%2E+Pediatric+nursing%2C+33%285%29%2E)
28. Hunter Lang, Monica Agrawal, Yoon Kim, and David Sontag. 2022a. Co-training improves prompt-based learning for large language models. arXiv preprint arXiv:2202.00828. — [arXiv:2202.00828](https://arxiv.org/abs/2202.00828) [find](https://scholar.google.com/scholar?q=Hunter+Lang%2C+Monica+Agrawal%2C+Yoon+Kim%2C+and+David+Sontag%2E+2022a%2E+Co%2Dtraining+improves+prompt%2Dbased+learning+for+large+language+models%2E+arXiv+preprint+arXiv%3A2202%2E00828%2E+%E2%80%94+%5BarXiv%3A2202%2E00828%5D%28https%3A%2F%2Farxi)
29. Hunter Lang, Aravindan Vijayaraghavan, and David Sontag. 2022b. Training subset selection for weak supervision. arXiv preprint arXiv:2206.02914. — [arXiv:2206.02914](https://arxiv.org/abs/2206.02914) [find](https://scholar.google.com/scholar?q=Hunter+Lang%2C+Aravindan+Vijayaraghavan%2C+and+David+Sontag%2E+2022b%2E+Training+subset+selection+for+weak+supervision%2E+arXiv+preprint+arXiv%3A2206%2E02914%2E+%E2%80%94+%5BarXiv%3A2206%2E02914%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2206%2E02914%29)
30. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2020. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240. [find](https://scholar.google.com/scholar?q=Jinhyuk+Lee%2C+Wonjin+Yoon%2C+Sungdong+Kim%2C+Donghyeon+Kim%2C+Sunkyu+Kim%2C+Chan+Ho+So%2C+and+Jaewoo+Kang%2E+2020%2E+Biobert%3A+a+pre%2Dtrained+biomedical+language+representation+model+for+biomedical+text+mining%2E+Bioinf) [our card](https://md.research.elamur.ai/free/en/papers/lee-2019-biobert-pre-trained-biomedical-language/)
31. Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017. End-to-end neural coreference resolution. arXiv preprint arXiv:1707.07045. [find](https://scholar.google.com/scholar?q=Kenton+Lee%2C+Luheng+He%2C+Mike+Lewis%2C+and+Luke+Zettlemoyer%2E+2017%2E+End%2Dto%2Dend+neural+coreference+resolution%2E+arXiv+preprint+arXiv%3A1707%2E07045%2E) [our card](https://md.research.elamur.ai/free/en/papers/lee-2017-end-end-neural-coreference-resolution/)
32. Ariel Levy, Monica Agrawal, Arvind Satyanarayan, and David Sontag. 2021. Assessing the impact of automated suggestions on decision making: Domain experts mediate model errors but take less initiative. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pages 1–13. [find](https://scholar.google.com/scholar?q=Ariel+Levy%2C+Monica+Agrawal%2C+Arvind+Satyanarayan%2C+and+David+Sontag%2E+2021%2E+Assessing+the+impact+of+automated+suggestions+on+decision+making%3A+Domain+experts+mediate+model+errors+but+take+less+initiative%2E)
33. Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. 2019a. A unified mrc framework for named entity recognition. arXiv preprint arXiv:1910.11476. [find](https://scholar.google.com/scholar?q=Xiaoya+Li%2C+Jingrong+Feng%2C+Yuxian+Meng%2C+Qinghong+Han%2C+Fei+Wu%2C+and+Jiwei+Li%2E+2019a%2E+A+unified+mrc+framework+for+named+entity+recognition%2E+arXiv+preprint+arXiv%3A1910%2E11476%2E) [our card](https://md.research.elamur.ai/free/en/papers/li-2020-unified-mrc-framework-named-entity/)
34. Xiaoya Li, Fan Yin, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, and Jiwei Li. 2019b. Entity-relation extraction as multi-turn question answering. arXiv preprint arXiv:1905.05529. [find](https://scholar.google.com/scholar?q=Xiaoya+Li%2C+Fan+Yin%2C+Zijun+Sun%2C+Xiayu+Li%2C+Arianna+Yuan%2C+Duo+Chai%2C+Mingxin+Zhou%2C+and+Jiwei+Li%2E+2019b%2E+Entity%2Drelation+extraction+as+multi%2Dturn+question+answering%2E+arXiv+preprint+arXiv%3A1905%2E05529%2E) [our card](https://md.research.elamur.ai/free/en/papers/li-2019-entity-relation-extraction-as-multi/)
35. Yen-Fu Luo, Sam Henry, Yanshan Wang, Feichen Shen, Ozlem Uzuner, and Anna Rumshisky. 2020. [find](https://scholar.google.com/scholar?q=Yen%2DFu+Luo%2C+Sam+Henry%2C+Yanshan+Wang%2C+Feichen+Shen%2C+Ozlem+Uzuner%2C+and+Anna+Rumshisky%2E+2020%2E)
36. The 2019 n2c2/umass lowell shared task on clinical concept normalization. Journal of the American Medical Informatics Association, 27(10):1529–e1. [find](https://scholar.google.com/scholar?q=The+2019+n2c2%2Fumass+lowell+shared+task+on+clinical+concept+normalization%2E+Journal+of+the+American+Medical+Informatics+Association%2C+27%2810%29%3A1529%E2%80%93e1%2E)
37. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26. [find](https://scholar.google.com/scholar?q=Tomas+Mikolov%2C+Ilya+Sutskever%2C+Kai+Chen%2C+Greg+S+Corrado%2C+and+Jeff+Dean%2E+2013%2E+Distributed+representations+of+words+and+phrases+and+their+compositionality%2E+Advances+in+neural+information+processing+sys)
38. Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022. Rethinking the role of demonstrations: What makes in-context learning work? arXiv preprint arXiv:2202.12837. [find](https://scholar.google.com/scholar?q=Sewon+Min%2C+Xinxi+Lyu%2C+Ari+Holtzman%2C+Mikel+Artetxe%2C+Mike+Lewis%2C+Hannaneh+Hajishirzi%2C+and+Luke+Zettlemoyer%2E+2022%2E+Rethinking+the+role+of+demonstrations%3A+What+makes+in%2Dcontext+learning+work%3F+arXiv+prepri) [our card](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/)
39. Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. 2021. Reframing instructional prompts to gptk’s language. arXiv preprint arXiv:2109.07830. [find](https://scholar.google.com/scholar?q=Swaroop+Mishra%2C+Daniel+Khashabi%2C+Chitta+Baral%2C+Yejin+Choi%2C+and+Hannaneh+Hajishirzi%2E+2021%2E+Reframing+instructional+prompts+to+gptk%E2%80%99s+language%2E+arXiv+preprint+arXiv%3A2109%2E07830%2E) [our card](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/)
40. Sungrim Moon, Serguei Pakhomov, Nathan Liu, James O Ryan, and Genevieve B Melton. 2014. A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources. Journal of the American Medical Informatics Association, 21(2):299–307. [find](https://scholar.google.com/scholar?q=Sungrim+Moon%2C+Serguei+Pakhomov%2C+Nathan+Liu%2C+James+O+Ryan%2C+and+Genevieve+B+Melton%2E+2014%2E+A+sense+inventory+for+clinical+abbreviations+and+acronyms+created+using+clinical+notes+and+medical+dictionary+re)
41. Milad Moradi, Kathrin Blagec, Florian Haberl, and Matthias Samwald. 2021. Gpt-3 models are poor few-shot learners in the biomedical domain. arXiv preprint arXiv:2109.02555. — [arXiv:2109.02555](https://arxiv.org/abs/2109.02555) [find](https://scholar.google.com/scholar?q=Milad+Moradi%2C+Kathrin+Blagec%2C+Florian+Haberl%2C+and+Matthias+Samwald%2E+2021%2E+Gpt%2D3+models+are+poor+few%2Dshot+learners+in+the+biomedical+domain%2E+arXiv+preprint+arXiv%3A2109%2E02555%2E+%E2%80%94+%5BarXiv%3A2109%2E02555%5D%28https%3A)
42. Danielle L Mowery, Brett R South, Lee Christensen, Jianwei Leng, Laura-Maria Peltonen, Sanna Salanterä, Hanna Suominen, David Martinez, Sumithra Velupillai, Noémie Elhadad, et al. 2016. Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: Share/clef ehealth challeng… [find](https://scholar.google.com/scholar?q=Danielle+L+Mowery%2C+Brett+R+South%2C+Lee+Christensen%2C+Jianwei+Leng%2C+Laura%2DMaria+Peltonen%2C+Sanna+Salanter%C3%A4%2C+Hanna+Suominen%2C+David+Martinez%2C+Sumithra+Velupillai%2C+No%C3%A9mie+Elhadad%2C+et+al%2E+2016%2E+Normalizing+ac)
43. Shawn N Murphy, Griffin Weber, Michael Mendis, Vivian Gainer, Henry C Chueh, Susanne Churchill, and Isaac Kohane. 2010. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). Journal of the American Medical Informatics Association, 17(2):124–130. [find](https://scholar.google.com/scholar?q=Shawn+N+Murphy%2C+Griffin+Weber%2C+Michael+Mendis%2C+Vivian+Gainer%2C+Henry+C+Chueh%2C+Susanne+Churchill%2C+and+Isaac+Kohane%2E+2010%2E+Serving+the+enterprise+and+beyond+with+informatics+for+integrating+biology+and+t)
44. Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019. ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 319–327, Florence, Italy. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Mark+Neumann%2C+Daniel+King%2C+Iz+Beltagy%2C+and+Waleed+Ammar%2E+2019%2E+ScispaCy%3A+Fast+and+Robust+Models+for+Biomedical+Natural+Language+Processing%2E+In+Proceedings+of+the+18th+BioNLP+Workshop+and+Shared+Task%2C+) [our card](https://md.research.elamur.ai/free/en/papers/neumann-2019-scispacy-fast-robust-models-biomedical/)
45. Aurélie Névéol, Hercules Dalianis, Sumithra Velupillai, Guergana Savova, and Pierre Zweigenbaum. 2018. Clinical natural language processing in languages other than english: opportunities and challenges. Journal of biomedical semantics, 9(1):1–13. [find](https://scholar.google.com/scholar?q=Aur%C3%A9lie+N%C3%A9v%C3%A9ol%2C+Hercules+Dalianis%2C+Sumithra+Velupillai%2C+Guergana+Savova%2C+and+Pierre+Zweigenbaum%2E+2018%2E+Clinical+natural+language+processing+in+languages+other+than+english%3A+opportunities+and+challenge)
46. Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain J Marshall, Ani Nenkova, and Byron C Wallace. 2018. A corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature. In Proceedings of the conference. Association for Compu… [find](https://scholar.google.com/scholar?q=Benjamin+Nye%2C+Junyi+Jessy+Li%2C+Roma+Patel%2C+Yinfei+Yang%2C+Iain+J+Marshall%2C+Ani+Nenkova%2C+and+Byron+C+Wallace%2E+2018%2E+A+corpus+with+multi%2Dlevel+annotations+of+patients%2C+interventions+and+outcomes+to+support) [our card](https://md.research.elamur.ai/free/en/papers/nye-2018-corpus-multi-level-annotations-patients/)
47. Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. arXiv preprint arXiv:2203.02155. [find](https://scholar.google.com/scholar?q=Long+Ouyang%2C+Jeff+Wu%2C+Xu+Jiang%2C+Diogo+Almeida%2C+Carroll+L+Wainwright%2C+Pamela+Mishkin%2C+Chong+Zhang%2C+Sandhini+Agarwal%2C+Katarina+Slama%2C+Alex+Ray%2C+et+al%2E+2022%2E+Training+language+models+to+follow+instructio)
48. Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Techno… [find](https://scholar.google.com/scholar?q=Matthew+E%2E+Peters%2C+Mark+Neumann%2C+Mohit+Iyyer%2C+Matt+Gardner%2C+Christopher+Clark%2C+Kenton+Lee%2C+and+Luke+Zettlemoyer%2E+2018%2E+Deep+contextualized+word+representations%2E+In+Proceedings+of+the+2018+Conference+o)
49. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D Manning. 2020. Stanza: A python natural language processing toolkit for many human languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 101–108. [find](https://scholar.google.com/scholar?q=Peng+Qi%2C+Yuhao+Zhang%2C+Yuhui+Zhang%2C+Jason+Bolton%2C+and+Christopher+D+Manning%2E+2020%2E+Stanza%3A+A+python+natural+language+processing+toolkit+for+many+human+languages%2E+In+Proceedings+of+the+58th+Annual+Meeti) [our card](https://md.research.elamur.ai/free/en/papers/ud-2020-qi-stanza-python-natural-language-processing/)
50. Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017. Snorkel: Rapid training data creation with weak supervision. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases, volume 11, page 269. NIH Public Access. [find](https://scholar.google.com/scholar?q=Alexander+Ratner%2C+Stephen+H+Bach%2C+Henry+Ehrenberg%2C+Jason+Fries%2C+Sen+Wu%2C+and+Christopher+R%C3%A9%2E+2017%2E+Snorkel%3A+Rapid+training+data+creation+with+weak+supervision%2E+In+Proceedings+of+the+VLDB+Endowment%2E+Int)
51. Kirk Roberts. 2016. Assessing the corpus size vs. similarity trade-off for word embeddings in clinical nlp. In Proceedings of the Clinical Natural Language Processing Workshop (ClinicalNLP), pages 54–63. [find](https://scholar.google.com/scholar?q=Kirk+Roberts%2E+2016%2E+Assessing+the+corpus+size+vs%2E+similarity+trade%2Doff+for+word+embeddings+in+clinical+nlp%2E+In+Proceedings+of+the+Clinical+Natural+Language+Processing+Workshop+%28ClinicalNLP%29%2C+pages+54%E2%80%93)
52. David L Sackett. 1997. Evidence-based medicine. In Seminars in perinatology, volume 21, pages 3–5. Elsevier. [find](https://scholar.google.com/scholar?q=David+L+Sackett%2E+1997%2E+Evidence%2Dbased+medicine%2E+In+Seminars+in+perinatology%2C+volume+21%2C+pages+3%E2%80%935%2E+Elsevier%2E)
53. Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021. Multitask prompted training enables zero-shot task generalization. arXiv preprint arXiv:2110.08207. [find](https://scholar.google.com/scholar?q=Victor+Sanh%2C+Albert+Webson%2C+Colin+Raffel%2C+Stephen+H+Bach%2C+Lintang+Sutawika%2C+Zaid+Alyafeai%2C+Antoine+Chaffin%2C+Arnaud+Stiegler%2C+Teven+Le+Scao%2C+Arun+Raja%2C+et+al%2E+2021%2E+Multitask+prompted+training+enables+) [our card](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
54. Timo Schick and Hinrich Schütze. 2021. Exploiting cloze-questions for few-shot text classification and natural language inference. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 255–269. [find](https://scholar.google.com/scholar?q=Timo+Schick+and+Hinrich+Sch%C3%BCtze%2E+2021%2E+Exploiting+cloze%2Dquestions+for+few%2Dshot+text+classification+and+natural+language+inference%2E+In+Proceedings+of+the+16th+Conference+of+the+European+Chapter+of+the+) [our card](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/)
55. Peng Shi and Jimmy Lin. 2019. Simple bert models for relation extraction and semantic role labeling. arXiv preprint arXiv:1904.05255. [find](https://scholar.google.com/scholar?q=Peng+Shi+and+Jimmy+Lin%2E+2019%2E+Simple+bert+models+for+relation+extraction+and+semantic+role+labeling%2E+arXiv+preprint+arXiv%3A1904%2E05255%2E) [our card](https://md.research.elamur.ai/free/en/papers/shi-2019-simple-bert-models-relation-extraction/)
56. Lotan Shilo and Gila Shilo. 2018. Analysis of abbreviations used by residents in admission notes and discharge summaries. QJM: An International Journal of Medicine, 111(3):179–183. [find](https://scholar.google.com/scholar?q=Lotan+Shilo+and+Gila+Shilo%2E+2018%2E+Analysis+of+abbreviations+used+by+residents+in+admission+notes+and+discharge+summaries%2E+QJM%3A+An+International+Journal+of+Medicine%2C+111%283%29%3A179%E2%80%93183%2E)
57. Yuqi Si and Kirk Roberts. 2019. Deep patient representation of clinical notes via multi-task learning for mortality prediction. AMIA Summits on Translational Science Proceedings, 2019:779. [find](https://scholar.google.com/scholar?q=Yuqi+Si+and+Kirk+Roberts%2E+2019%2E+Deep+patient+representation+of+clinical+notes+via+multi%2Dtask+learning+for+mortality+prediction%2E+AMIA+Summits+on+Translational+Science+Proceedings%2C+2019%3A779%2E)
58. Marta Skreta, Aryan Arbabi, Jixuan Wang, Erik Drysdale, Jacob Kelly, Devin Singh, and Michael Brudno. 2021. Automatically disambiguating medical acronyms with ontology-aware deep learning. Nature communications, 12(1):1–10. [find](https://scholar.google.com/scholar?q=Marta+Skreta%2C+Aryan+Arbabi%2C+Jixuan+Wang%2C+Erik+Drysdale%2C+Jacob+Kelly%2C+Devin+Singh%2C+and+Michael+Brudno%2E+2021%2E+Automatically+disambiguating+medical+acronyms+with+ontology%2Daware+deep+learning%2E+Nature+comm)
59. Ryan Smith, Jason A Fries, Braden Hancock, and Stephen H Bach. 2022. Language models in the loop: Incorporating prompting into weak supervision. arXiv preprint arXiv:2205.02318. — [arXiv:2205.02318](https://arxiv.org/abs/2205.02318) [find](https://scholar.google.com/scholar?q=Ryan+Smith%2C+Jason+A+Fries%2C+Braden+Hancock%2C+and+Stephen+H+Bach%2E+2022%2E+Language+models+in+the+loop%3A+Incorporating+prompting+into+weak+supervision%2E+arXiv+preprint+arXiv%3A2205%2E02318%2E+%E2%80%94+%5BarXiv%3A2205%2E02318%5D%28h)
60. Sunghwan Sohn, Cheryl Clark, Scott R Halgrim, Sean P Murphy, Christopher G Chute, and Hongfang Liu. 2014. Medxn: an open source medication extraction and normalization tool for clinical text. Journal of the American Medical Informatics Association, 21(5):858–865. [find](https://scholar.google.com/scholar?q=Sunghwan+Sohn%2C+Cheryl+Clark%2C+Scott+R+Halgrim%2C+Sean+P+Murphy%2C+Christopher+G+Chute%2C+and+Hongfang+Liu%2E+2014%2E+Medxn%3A+an+open+source+medication+extraction+and+normalization+tool+for+clinical+text%2E+Journal+)
61. Shubham Toshniwal, Sam Wiseman, Allyson Ettinger, Karen Livescu, and Kevin Gimpel. 2020. Learning to ignore: Long document coreference with bounded memory neural networks. arXiv preprint arXiv:2010.02807. [find](https://scholar.google.com/scholar?q=Shubham+Toshniwal%2C+Sam+Wiseman%2C+Allyson+Ettinger%2C+Karen+Livescu%2C+and+Kevin+Gimpel%2E+2020%2E+Learning+to+ignore%3A+Long+document+coreference+with+bounded+memory+neural+networks%2E+arXiv+preprint+arXiv%3A2010%2E02) [our card](https://md.research.elamur.ai/free/en/papers/toshniwal-2020-learning-ignore-long-document-coreference/)
62. Shubham Toshniwal, Patrick Xia, Sam Wiseman, Karen Livescu, and Kevin Gimpel. 2021. On generalization in coreference resolution. arXiv preprint arXiv:2109.09667. [find](https://scholar.google.com/scholar?q=Shubham+Toshniwal%2C+Patrick+Xia%2C+Sam+Wiseman%2C+Karen+Livescu%2C+and+Kevin+Gimpel%2E+2021%2E+On+generalization+in+coreference+resolution%2E+arXiv+preprint+arXiv%3A2109%2E09667%2E) [our card](https://md.research.elamur.ai/free/en/papers/toshniwal-2021-generalization-coreference-resolution/)
63. Ozlem Uzuner, Andreea Bodnari, Shuying Shen, Tyler Forbush, John Pestian, and Brett R South. 2012. Evaluating the state of the art in coreference resolution for electronic medical records. Journal of the American Medical Informatics Association, 19(5):786–791. [find](https://scholar.google.com/scholar?q=Ozlem+Uzuner%2C+Andreea+Bodnari%2C+Shuying+Shen%2C+Tyler+Forbush%2C+John+Pestian%2C+and+Brett+R+South%2E+2012%2E+Evaluating+the+state+of+the+art+in+coreference+resolution+for+electronic+medical+records%2E+Journal+of+)
64. Özlem Uzuner, Imre Solti, and Eithon Cadag. 2010. Extracting medication information from clinical text. Journal of the American Medical Informatics Association, 17(5):514–518. [find](https://scholar.google.com/scholar?q=%C3%96zlem+Uzuner%2C+Imre+Solti%2C+and+Eithon+Cadag%2E+2010%2E+Extracting+medication+information+from+clinical+text%2E+Journal+of+the+American+Medical+Informatics+Association%2C+17%285%29%3A514%E2%80%93518%2E)
65. Mathias Verbeke, Vincent Van Asch, Roser Morante, Paolo Frasconi, Walter Daelemans, and Luc De Raedt. 2012. A statistical relational learning approach to identifying evidence based medicine categories. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing an… [find](https://scholar.google.com/scholar?q=Mathias+Verbeke%2C+Vincent+Van+Asch%2C+Roser+Morante%2C+Paolo+Frasconi%2C+Walter+Daelemans%2C+and+Luc+De+Raedt%2E+2012%2E+A+statistical+relational+learning+approach+to+identifying+evidence+based+medicine+categories)
66. Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021. Want to reduce labeling cost? gpt-3 can help. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4195–4205. [find](https://scholar.google.com/scholar?q=Shuohang+Wang%2C+Yang+Liu%2C+Yichong+Xu%2C+Chenguang+Zhu%2C+and+Michael+Zeng%2E+2021%2E+Want+to+reduce+labeling+cost%3F+gpt%2D3+can+help%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2021%2C+page) [our card](https://md.research.elamur.ai/free/en/papers/wang-2021-want-reduce-labeling-cost-gpt/)
67. Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, et al. 2018. Clinical information extraction applications: a literature review. Journal of biomedical informatics, 77:34–49. [find](https://scholar.google.com/scholar?q=Yanshan+Wang%2C+Liwei+Wang%2C+Majid+Rastegar%2DMojarad%2C+Sungrim+Moon%2C+Feichen+Shen%2C+Naveed+Afzal%2C+Sijia+Liu%2C+Yuqun+Zeng%2C+Saeed+Mehrabi%2C+Sunghwan+Sohn%2C+et+al%2E+2018%2E+Clinical+information+extraction+applicatio)
68. Marc Weeber, James G Mork, and Alan R Aronson. 2001. Developing a test collection for biomedical word sense disambiguation. In Proceedings of the AMIA Symposium, page 746. American Medical Informatics Association. [find](https://scholar.google.com/scholar?q=Marc+Weeber%2C+James+G+Mork%2C+and+Alan+R+Aronson%2E+2001%2E+Developing+a+test+collection+for+biomedical+word+sense+disambiguation%2E+In+Proceedings+of+the+AMIA+Symposium%2C+page+746%2E+American+Medical+Informatics)
69. Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2021. Fine-tuned language models are zero-shot learners. arXiv:2109.01652. [find](https://scholar.google.com/scholar?q=Jason+Wei%2C+Maarten+Bosma%2C+Vincent+Y%2E+Zhao%2C+Kelvin+Guu%2C+Adams+Wei+Yu%2C+Brian+Lester%2C+Nan+Du%2C+Andrew+M%2E+Dai%2C+and+Quoc+V%2E+Le%2E+2021%2E+Fine%2Dtuned+language+models+are+zero%2Dshot+learners%2E+arXiv%3A2109%2E01652%2E)
70. Fei Xia and Meliha Yetisgen-Yildiz. 2012. Clinical corpus annotation: challenges and strategies. In Proceedings of the Third Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM’2012) in conjunction with the International Conference on Language Resources and Evaluation (… [find](https://scholar.google.com/scholar?q=Fei+Xia+and+Meliha+Yetisgen%2DYildiz%2E+2012%2E+Clinical+corpus+annotation%3A+challenges+and+strategies%2E+In+Proceedings+of+the+Third+Workshop+on+Building+and+Evaluating+Resources+for+Biomedical+Text+Mining+%28B)
71. Xiaohan Yang, Eduardo Peynetti, Vasco Meerman, and Chris Tanner. 2022. What gpt knows about who is who. arXiv preprint arXiv:2205.07407. [find](https://scholar.google.com/scholar?q=Xiaohan+Yang%2C+Eduardo+Peynetti%2C+Vasco+Meerman%2C+and+Chris+Tanner%2E+2022%2E+What+gpt+knows+about+who+is+who%2E+arXiv+preprint+arXiv%3A2205%2E07407%2E)
72. Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner. 2021. Wrench: A comprehensive benchmark for weak supervision. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2). [find](https://scholar.google.com/scholar?q=Jieyu+Zhang%2C+Yue+Yu%2C+Yinghao+Li%2C+Yujing+Wang%2C+Yaming+Yang%2C+Mao+Yang%2C+and+Alexander+Ratner%2E+2021%2E+Wrench%3A+A+comprehensive+benchmark+for+weak+supervision%2E+In+Thirty%2Dfifth+Conference+on+Neural+Informatio)
73. Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:2205.01068. [find](https://scholar.google.com/scholar?q=Susan+Zhang%2C+Stephen+Roller%2C+Naman+Goyal%2C+Mikel+Artetxe%2C+Moya+Chen%2C+Shuohui+Chen%2C+Christopher+Dewan%2C+Mona+Diab%2C+Xian+Li%2C+Xi+Victoria+Lin%2C+et+al%2E+2022%2E+Opt%3A+Open+pre%2Dtrained+transformer+language+models)
74. Zachariah Zhang, Jingshu Liu, and Narges Razavian. 2020. Bert-xml: Large scale automated icd coding using bert pretraining. arXiv preprint arXiv:2006.03685. [find](https://scholar.google.com/scholar?q=Zachariah+Zhang%2C+Jingshu+Liu%2C+and+Narges+Razavian%2E+2020%2E+Bert%2Dxml%3A+Large+scale+automated+icd+coding+using+bert+pretraining%2E+arXiv+preprint+arXiv%3A2006%2E03685%2E)
75. Jiaping Zheng, Wendy W Chapman, Rebecca S Crowley, and Guergana K Savova. 2011. Coreference resolution: A review of general methodologies and applications in the clinical domain. Journal of biomedical informatics, 44(6):1113–1122. [find](https://scholar.google.com/scholar?q=Jiaping+Zheng%2C+Wendy+W+Chapman%2C+Rebecca+S+Crowley%2C+and+Guergana+K+Savova%2E+2011%2E+Coreference+resolution%3A+A+review+of+general+methodologies+and+applications+in+the+clinical+domain%2E+Journal+of+biomedical)
76. Pierre Zweigenbaum, Dina Demner-Fushman, Hong Yu, and Kevin B Cohen. 2007. Frontiers of biomedical text mining: current progress. Briefings in bioinformatics, 8(5):358–375. [find](https://scholar.google.com/scholar?q=Pierre+Zweigenbaum%2C+Dina+Demner%2DFushman%2C+Hong+Yu%2C+and+Kevin+B+Cohen%2E+2007%2E+Frontiers+of+biomedical+text+mining%3A+current+progress%2E+Briefings+in+bioinformatics%2C+8%285%29%3A358%E2%80%93375%2E)
77. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019. Huggingface’s transformers: State-ofthe-art natural language processing. arXiv preprint arXiv:1910.03771. [find](https://scholar.google.com/scholar?q=Thomas+Wolf%2C+Lysandre+Debut%2C+Victor+Sanh%2C+Julien+Chaumond%2C+Clement+Delangue%2C+Anthony+Moi%2C+Pier%2Dric+Cistac%2C+Tim+Rault%2C+R%C3%A9mi+Louf%2C+Morgan+Funtowicz%2C+et+al%2E+2019%2E+Huggingface%E2%80%99s+transformers%3A+State%2Dofthe%2D) [our card](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
78. Stephen Wu, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei, Yang Xiang, et al. 2020. Deep learning in clinical natural language processing: a methodical review. Journal of the American Medical Informatics Association, 27(3):457–470. [find](https://scholar.google.com/scholar?q=Stephen+Wu%2C+Kirk+Roberts%2C+Surabhi+Datta%2C+Jingcheng+Du%2C+Zongcheng+Ji%2C+Yuqi+Si%2C+Sarvesh+Soni%2C+Qiong+Wang%2C+Qiang+Wei%2C+Yang+Xiang%2C+et+al%2E+2020%2E+Deep+learning+in+clinical+natural+language+processing%3A+a+met)
79. Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022. Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. In CHI Conference on Human Factors in Computing Systems, pages 1–22. [find](https://scholar.google.com/scholar?q=Tongshuang+Wu%2C+Michael+Terry%2C+and+Carrie+Jun+Cai%2E+2022%2E+Ai+chains%3A+Transparent+and+controllable+human%2Dai+interaction+by+chaining+large+language+model+prompts%2E+In+CHI+Conference+on+Human+Factors+in+Com)
80. Yonghui Wu, Jun Xu, Yaoyun Zhang, and Hua Xu. 2015. Clinical abbreviation disambiguation using neural word embeddings. In Proceedings of BioNLP 15, pages 171–176. [find](https://scholar.google.com/scholar?q=Yonghui+Wu%2C+Jun+Xu%2C+Yaoyun+Zhang%2C+and+Hua+Xu%2E+2015%2E+Clinical+abbreviation+disambiguation+using+neural+word+embeddings%2E+In+Proceedings+of+BioNLP+15%2C+pages+171%E2%80%93176%2E)